{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import quandl\n",
    "import datetime\n",
    "\n",
    "# We will look at stock prices over the past year, starting at January 1, 2016\n",
    "start = datetime.datetime(2016, 1, 1)\n",
    "end = datetime.date.today()\n",
    "\n",
    "# Let's get Apple stock data; Apple's ticker symbol is AAPL\n",
    "# First argument is the series we want, second is the source (\"yahoo\" for Yahoo! Finance), third is the start date, fourth is the end date\n",
    "s = \"AAPL\"\n",
    "apple = quandl.get(\"WIKI/\" + s, start_date=start, end_date=end)\n",
    "type(apple)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Ex-Dividend</th>\n",
       "      <th>Split Ratio</th>\n",
       "      <th>Adj. Open</th>\n",
       "      <th>Adj. High</th>\n",
       "      <th>Adj. Low</th>\n",
       "      <th>Adj. Close</th>\n",
       "      <th>Adj. Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>102.61</td>\n",
       "      <td>105.368</td>\n",
       "      <td>102.00</td>\n",
       "      <td>105.35</td>\n",
       "      <td>67649387.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>99.136516</td>\n",
       "      <td>101.801154</td>\n",
       "      <td>98.547165</td>\n",
       "      <td>101.783763</td>\n",
       "      <td>67649387.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>105.75</td>\n",
       "      <td>105.850</td>\n",
       "      <td>102.41</td>\n",
       "      <td>102.71</td>\n",
       "      <td>55790992.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>102.170223</td>\n",
       "      <td>102.266838</td>\n",
       "      <td>98.943286</td>\n",
       "      <td>99.233131</td>\n",
       "      <td>55790992.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>100.56</td>\n",
       "      <td>102.370</td>\n",
       "      <td>99.87</td>\n",
       "      <td>100.70</td>\n",
       "      <td>68457388.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>97.155911</td>\n",
       "      <td>98.904640</td>\n",
       "      <td>96.489269</td>\n",
       "      <td>97.291172</td>\n",
       "      <td>68457388.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>98.68</td>\n",
       "      <td>100.130</td>\n",
       "      <td>96.43</td>\n",
       "      <td>96.45</td>\n",
       "      <td>81094428.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>95.339552</td>\n",
       "      <td>96.740467</td>\n",
       "      <td>93.165717</td>\n",
       "      <td>93.185040</td>\n",
       "      <td>81094428.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>98.55</td>\n",
       "      <td>99.110</td>\n",
       "      <td>96.76</td>\n",
       "      <td>96.96</td>\n",
       "      <td>70798016.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>95.213952</td>\n",
       "      <td>95.754996</td>\n",
       "      <td>93.484546</td>\n",
       "      <td>93.677776</td>\n",
       "      <td>70798016.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open     High     Low   Close      Volume  Ex-Dividend  \\\n",
       "Date                                                                   \n",
       "2016-01-04  102.61  105.368  102.00  105.35  67649387.0          0.0   \n",
       "2016-01-05  105.75  105.850  102.41  102.71  55790992.0          0.0   \n",
       "2016-01-06  100.56  102.370   99.87  100.70  68457388.0          0.0   \n",
       "2016-01-07   98.68  100.130   96.43   96.45  81094428.0          0.0   \n",
       "2016-01-08   98.55   99.110   96.76   96.96  70798016.0          0.0   \n",
       "\n",
       "            Split Ratio   Adj. Open   Adj. High   Adj. Low  Adj. Close  \\\n",
       "Date                                                                     \n",
       "2016-01-04          1.0   99.136516  101.801154  98.547165  101.783763   \n",
       "2016-01-05          1.0  102.170223  102.266838  98.943286   99.233131   \n",
       "2016-01-06          1.0   97.155911   98.904640  96.489269   97.291172   \n",
       "2016-01-07          1.0   95.339552   96.740467  93.165717   93.185040   \n",
       "2016-01-08          1.0   95.213952   95.754996  93.484546   93.677776   \n",
       "\n",
       "            Adj. Volume  \n",
       "Date                     \n",
       "2016-01-04   67649387.0  \n",
       "2016-01-05   55790992.0  \n",
       "2016-01-06   68457388.0  \n",
       "2016-01-07   81094428.0  \n",
       "2016-01-08   70798016.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21089c07748>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt #import matplotlib\n",
    "#this line is necessary for the plot to appear in a Jupyter notebook\n",
    "%matplotlib inline\n",
    "#control the default size of figures in this Jupyter notebook\n",
    "%pylab inline\n",
    "pylab.rcParams['figure.figsize']=(15,9) #change the size of plots\n",
    "apple['Adj. Close'].plot(grid=True)#Plot the adjusted close price of APPL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.dates import DateFormatter, WeekdayLocator, DayLocator, MONDAY\n",
    "from mpl_finance import candlestick_ohlc\n",
    " \n",
    "def pandas_candlestick_ohlc(dat, stick=\"day\", adj=False, otherseries=None):\n",
    "    \"\"\"\n",
    "    :param dat: pandas DataFrame object with datetime64 index, and float columns \"Open\", \"High\", \"Low\", and \"Close\", likely created via DataReader from \"yahoo\"\n",
    "    :param stick: A string or number indicating the period of time covered by a single candlestick. Valid string inputs include \"day\", \"week\", \"month\", and \"year\", (\"day\" default), and any numeric input indicates the number of trading days included in a period\n",
    "    :param adj: A boolean indicating whether to use adjusted prices\n",
    "    :param otherseries: An iterable that will be coerced into a list, containing the columns of dat that hold other series to be plotted as lines\n",
    " \n",
    "    This will show a Japanese candlestick plot for stock data stored in dat, also plotting other series if passed.\n",
    "    \"\"\"\n",
    "    mondays = WeekdayLocator(MONDAY)        # major ticks on the mondays\n",
    "    alldays = DayLocator()                  # minor ticks on the days\n",
    "    dayFormatter = DateFormatter('%d')      # e.g., 12\n",
    " \n",
    "    # Create a new DataFrame which includes OHLC data for each period specified\n",
    "    # by stick input\n",
    "    fields = [\"Open\", \"High\", \"Low\", \"Close\"]\n",
    "    if adj:\n",
    "        fields = [\"Adj. \" + s for s in fields]\n",
    "    transdat = dat.loc[:,fields]\n",
    "    transdat.columns = pd.Index([\"Open\", \"High\", \"Low\", \"Close\"])\n",
    "    if (type(stick) == str):\n",
    "        if stick == \"day\":\n",
    "            plotdat = transdat\n",
    "            stick = 1 # Used for plotting\n",
    "        elif stick in [\"week\", \"month\", \"year\"]:\n",
    "            if stick == \"week\":\n",
    "                transdat[\"week\"] = pd.to_datetime(transdat.index).map(lambda x: x.isocalendar()[1]) # Identify weeks\n",
    "            elif stick == \"month\":\n",
    "                transdat[\"month\"] = pd.to_datetime(transdat.index).map(lambda x: x.month) # Identify months\n",
    "            transdat[\"year\"] = pd.to_datetime(transdat.index).map(lambda x: x.isocalendar()[0]) # Identify years\n",
    "            grouped = transdat.groupby(list(set([\"year\",stick]))) # Group by year and other appropriate variable\n",
    "            plotdat = pd.DataFrame({\"Open\": [], \"High\": [], \"Low\": [], \"Close\": []}) # Create empty data frame containing what will be plotted\n",
    "            for name, group in grouped:\n",
    "                plotdat = plotdat.append(pd.DataFrame({\"Open\": group.iloc[0,0],\n",
    "                                            \"High\": max(group.High),\n",
    "                                            \"Low\": min(group.Low),\n",
    "                                            \"Close\": group.iloc[-1,3]},\n",
    "                                           index = [group.index[0]]))\n",
    "            if stick == \"week\": stick = 5\n",
    "            elif stick == \"month\": stick = 30\n",
    "            elif stick == \"year\": stick = 365\n",
    " \n",
    "    elif (type(stick) == int and stick >= 1):\n",
    "        transdat[\"stick\"] = [np.floor(i / stick) for i in range(len(transdat.index))]\n",
    "        grouped = transdat.groupby(\"stick\")\n",
    "        plotdat = pd.DataFrame({\"Open\": [], \"High\": [], \"Low\": [], \"Close\": []}) # Create empty data frame containing what will be plotted\n",
    "        for name, group in grouped:\n",
    "            plotdat = plotdat.append(pd.DataFrame({\"Open\": group.iloc[0,0],\n",
    "                                        \"High\": max(group.High),\n",
    "                                        \"Low\": min(group.Low),\n",
    "                                        \"Close\": group.iloc[-1,3]},\n",
    "                                       index = [group.index[0]]))\n",
    " \n",
    "    else:\n",
    "        raise ValueError('Valid inputs to argument \"stick\" include the strings \"day\", \"week\", \"month\", \"year\", or a positive integer')\n",
    " \n",
    " \n",
    "    # Set plot parameters, including the axis object ax used for plotting\n",
    "    fig, ax = plt.subplots()\n",
    "    fig.subplots_adjust(bottom=0.2)\n",
    "    if plotdat.index[-1] - plotdat.index[0] < pd.Timedelta('730 days'):\n",
    "        weekFormatter = DateFormatter('%b %d')  # e.g., Jan 12\n",
    "        ax.xaxis.set_major_locator(mondays)\n",
    "        ax.xaxis.set_minor_locator(alldays)\n",
    "    else:\n",
    "        weekFormatter = DateFormatter('%b %d, %Y')\n",
    "    ax.xaxis.set_major_formatter(weekFormatter)\n",
    " \n",
    "    ax.grid(True)\n",
    " \n",
    "    # Create the candelstick chart\n",
    "    candlestick_ohlc(ax, list(zip(list(date2num(plotdat.index.tolist())), plotdat[\"Open\"].tolist(), plotdat[\"High\"].tolist(),\n",
    "                      plotdat[\"Low\"].tolist(), plotdat[\"Close\"].tolist())),\n",
    "                      colorup = \"black\", colordown = \"red\", width = stick * .4)\n",
    " \n",
    "    # Plot other series (such as moving averages) as lines\n",
    "    if otherseries != None:\n",
    "        if type(otherseries) != list:\n",
    "            otherseries = [otherseries]\n",
    "        dat.loc[:,otherseries].plot(ax = ax, lw = 1.3, grid = True)\n",
    " \n",
    "    ax.xaxis_date()\n",
    "    ax.autoscale_view()\n",
    "    plt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right')\n",
    " \n",
    "    plt.show()\n",
    " \n",
    "pandas_candlestick_ohlc(apple, adj=True, stick=\"month\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>GOOG</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>101.783763</td>\n",
       "      <td>52.181598</td>\n",
       "      <td>741.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>99.233131</td>\n",
       "      <td>52.419653</td>\n",
       "      <td>742.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>97.291172</td>\n",
       "      <td>51.467434</td>\n",
       "      <td>743.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>93.185040</td>\n",
       "      <td>49.677262</td>\n",
       "      <td>726.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>93.677776</td>\n",
       "      <td>49.829617</td>\n",
       "      <td>714.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  AAPL       MSFT    GOOG\n",
       "Date                                     \n",
       "2016-01-04  101.783763  52.181598  741.84\n",
       "2016-01-05   99.233131  52.419653  742.58\n",
       "2016-01-06   97.291172  51.467434  743.62\n",
       "2016-01-07   93.185040  49.677262  726.39\n",
       "2016-01-08   93.677776  49.829617  714.47"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "microsoft, google = (quandl.get(\"WIKI/\" + s, start_date=start, end_date=end) for s in [\"MSFT\", \"GOOG\"])\n",
    "\n",
    "# Below I create a DataFrame consisting of the adjusted closing price of these stocks, first by making a list of these objects and using the join method\n",
    "stocks = pd.DataFrame({\"AAPL\": apple[\"Adj. Close\"],\n",
    "                      \"MSFT\": microsoft[\"Adj. Close\"],\n",
    "                      \"GOOG\": google[\"Adj. Close\"]})\n",
    "\n",
    "stocks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2108a0656a0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stocks.plot(grid = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2108a0beb00>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stocks.plot(secondary_y = [\"AAPL\", \"MSFT\"], grid = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>GOOG</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>-0.025059</td>\n",
       "      <td>0.004562</td>\n",
       "      <td>0.000998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>-0.044139</td>\n",
       "      <td>-0.013686</td>\n",
       "      <td>0.002399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>-0.084480</td>\n",
       "      <td>-0.047993</td>\n",
       "      <td>-0.020827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>-0.079639</td>\n",
       "      <td>-0.045073</td>\n",
       "      <td>-0.036895</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                AAPL      MSFT      GOOG\n",
       "Date                                    \n",
       "2016-01-04  0.000000  0.000000  0.000000\n",
       "2016-01-05 -0.025059  0.004562  0.000998\n",
       "2016-01-06 -0.044139 -0.013686  0.002399\n",
       "2016-01-07 -0.084480 -0.047993 -0.020827\n",
       "2016-01-08 -0.079639 -0.045073 -0.036895"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df.apply(arg) will apply the function arg to each column in df, and return a DataFrame with the result\n",
    "# Recall that lambda x is an anonymous function accepting parameter x; in this case, x will be a pandas Series object\n",
    "stock_return = stocks.apply(lambda x: x / x[0])\n",
    "stock_return.head() - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x2108a195940>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stock_return.plot(grid = True).axhline(y = 1, color = \"black\", lw = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>GOOG</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>-0.025379</td>\n",
       "      <td>0.004552</td>\n",
       "      <td>0.000997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>-0.019764</td>\n",
       "      <td>-0.018332</td>\n",
       "      <td>0.001400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>-0.043121</td>\n",
       "      <td>-0.035402</td>\n",
       "      <td>-0.023443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>0.005274</td>\n",
       "      <td>0.003062</td>\n",
       "      <td>-0.016546</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                AAPL      MSFT      GOOG\n",
       "Date                                    \n",
       "2016-01-04       NaN       NaN       NaN\n",
       "2016-01-05 -0.025379  0.004552  0.000997\n",
       "2016-01-06 -0.019764 -0.018332  0.001400\n",
       "2016-01-07 -0.043121 -0.035402 -0.023443\n",
       "2016-01-08  0.005274  0.003062 -0.016546"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    " \n",
    "stock_change = stocks.apply(lambda x: np.log(x) - np.log(x.shift(1))) # shift moves dates back by 1.\n",
    "stock_change.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x2108a195f98>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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l1L+Gy9QB0mRHIs6u+MA9Bwc/YzC9tUwVO93dtJ1wg01we6DGKrY3FpPZMSVJKUdR/3buY44YUSkMK7rKd04+FLLa2wRXL7BKc9vI6qOcKW5jvNnNXVPSCDufG7syZAvDI6DjbKi8hXqGkPQ12nCiiLIDDFXd3bu79AHTwGBwIekHP2O4vB6WfBbwGIPUrSQxs0yC297ODF+6Yw05rfTkGAvG4IWrMnq2duc+wiQ/QmYIT/MZRn8A8r3BYxvvht598Pwfh965EcL4I3a2KWmENYbF163illW7hnWNsTDL9ge37PZB4M9yQmszw8lhAJAmmhhMsOqhwBjAIhpYpiQIawzFxP8H927ggQ3NrNreXvJypjn6kyav+aJ5Ro0xWOMlhEnOGBnp2/OVDNOUZK/C58LU2RWPIcXYT9nxZh4Jh6sWdJPbn3llWLX8pZR0pAv0ZocX1jnexsp0aiUdBBqDI+PrB6g8fcCrmAaGYFBhSWP/lY0gtlZq/KO22rVPyzLO54JhMpUuKiitehv66Mc4F/xO21ebMTgTzb3PSGoMw3E++04KZT/vzLVzzuyZrNNDmsQYYHyROs+05ZjWft+wg//71wbufTFc5Hjg0J2Q72ES9nGmMOC8PQOTnozGtpa+A7QfOwxOxvcQNnUapoE+yKsdUozh6Ql5bq6rczUGyypTTOQLuskD1Zdw3IoLio4ByDGIcQ44n191jSHEGF4FjWHY4aohjaFds0J3u2TwfY6FvX+8ScFhjWFnu8VUhyNoaragMtwFxEY3k//AcB5HR3LTyp187A9PD++CWhZW/AS0kTXjHWnu5bOxB0sfLKRh73Nlzy2E1rM2DAP9sDQl2TCFRBdBH4NRIvW9oBm8c+5svhbbV/o6YxCxUNDHwJTkcz6PmMZg+22GZFvuhzHkTIvoaSFqNxY27HHGF4qczx0pa/5PGsbCS4XhmAQDfRtfg+UIEiaS/T1ZujLa8NY82PssPPEL2Lt6ZDq4/wXobeLn+Z8yV2kp3eb+b8If3wPde0seDvuapKHbzueBSwqHFGMwhEU4pGF9GBLQS5iFTNvUtK6idAVQQxuYKenRzS0jVg01YIIZdeezRDMLwcTAIWJYpT36cT5n7RBkPTS59f5utOeZQBTTiCDVWuwYH2OEnc+d6UHMye2PQm+xyckzbR5apiTHx2AKk7Y+iw70DMeP4piqR8rxf+Mi+PVJQD8my45t1v+expKHw9F6hnmYawyGkJZEaRMRSWl/gaqXSALxX8csQLYL7vsKFEq37clqXHjr81x46/PD7jd4zmc5Fs5nRiZBqqCb7O+xJuXwnc9BH0PeVo/10Izt9z63vA+ue9Pg+9EfrjqWxA0lYsXHAKY0+fzDn2fVvlWA9/4cxtAv03Rw56dg9Q1Fu4eVj+LDeAvtdXpjCOkyht7cMBiD83wjGRFmlGDswmf5qJpi/c+UDp4JawymbliWlNF2PgshzhJCbBVCbBdCFC3CI4RICiHutI+vFkLMs/efKYRYI4RYb/9/13D6YQAaIG0iYiJKMgZF67+ss6EX4LGfwZo/w4t/Kd1IwrGike37S7+cwSJvBE1JJ86fy8/qB1hbf8Pd8IujLYaY74N/fIaKbBk1FHzOZ3tSC+vew80L2LC/h5w2HB+DP48hbEoqrTGMhSlJSfUztqOIvkIfzzQ9w9aurYBXEqMjbf03BhJdp2WtNYHDu0eoqON4y3x2wlVNGBmNwYluGmEfg3TFNQsxxVcep3qy9T9dhjGEBLwxcT4LIVTgeuD9wALgfCHEglCzC4EuKeUxwDXAlfb+duBsKeWJWEt/DquKnSGkFdrlMyWV1BjM/k0BmqF5EoCilr5XrpdHkt/mJ1w/nC67cGzzVh6D9Qr/Wlc7sJOXfcuSHrJd8NzNsPEeZu5fduDzfM5nGD5j8JvVhkQQ/BpDSGrK2owh/DYHzRhefmjkzUtjhPD7cgiCI+07/8vCtNOecsVrnTvO5+E6j19tvv30/qd5ofWFAbd3GIMhpMsQhmdKshnDq5B17h+6SsUnKFXZjGGAGoM0bY1hEPceCY1hIbBdSrlTSlkA/g4sDrVZDNxqby8B3i2EEFLKF6SUjoFzI1AhhBjU0l9b43G6FOsxDAGGEJimz/lcIsJINfsPUTP0vGuOQk3QnSmQygevY2rWR3masnEw3S0Lx6ZrSIk55DouApo3AJCrmD6A9p7zGYbPGAq6N/WG7WMIjYGrMYTU4UEzhr9+bOTNSwdAtmDwiwe3lE2o9KO1L8e8797PEy+3HbBtuC7OlpZO9nZ6+9yxKWRKF5N0xjhXvMDPsPJR/Ld4lTnDRY9cxKcf+PSA23uMwTO1DSlXY9ty0AvenB0sY+jcBU3r+m3iH7k3xjZ4tEa1gwpSpedIKR+DMUiNYSTKbs8C/O7xRiBshHXbSCl1IUQPMBlLY3Dwn8ALUsqSyQVCiIuAiwCmT59OQ0MDSMkls49ghq7z/YYGDDtTsKu7Daqsgb139cs0bHiIC0/0+I2qe6akhoaGonu9uG4tNc2NpOIx7nn6Sq67s44KVeGGM6vdNum+bj4IqJikUqmS1xkMevqsD3rzli2savckuIFc93RNIwGsemoVx7S2MB0o5DJF535vZYb3zVP5UCLBm3I6a9euAea6GsOqZ1exPzn02PeXmjxivm/ffhoaOoralBurVq2Vbc338T2sYNrGPa+w3deurbcLYtDU1hI4f+WqVUxMlpZvFtn//e1L7euvX+Wu6WAg59y9rcDSHRpdzXt537x4v21faLXG8Ff3reHzx+v9Xn93fnfgd0cmxWf+4BVD3LBpExN7trGoYTEt097B5gVfD7RXjBxvB/ra9rEmdJ8d3RYj2d/UTENDV+DYYOZ7a8Zj9uXOWdK5hHWZdVwx+4oBXbMUGhoa6OnrYdmKZVQpVWXb6bZ5zaEVAM+/tIm67m0DvldVeg8Ln7uEjRPfTdfkN/FWYMfWjezNNhS1LTdWixos2blh0b3B/fZ/UwajiC5J3smbZZ6GhoXM372TI4HNm9axN/kYCTUoLT334nPkX/bIaGzfpiKB6kAYCcZQ6pZh5tRvGyHEa7HMS+8tdxMp5Y3AjQCnnHKKXLRokSUF3Q7NsRiLFi3iSvvdVlYnQFo+hv19Giu7dW6/5H3utW5/7D53e9GiRd5NbJ3mhBNOYLo+ic8kptIYhynqPtqNOYG2zfv3whqrDEdNTU3wOoPB0kusMYgtBvIcd9zxLDxWwtIS/SuHZ2OgwRmnnQbph6AVKuMicK6Ukv0PLuMvLctYcsQU7tjfzMlveD083YXzKhactIBTjxi6Y7XrhUZYZ0lBM2YcwaJFJ3kH216G+vk0rFxV8pk+ePcH2WPu4fOqyjTDYPbMGcz2tVvdXAF5mFg/iXe84x1wm7X/LW85nRl1FaU71GD9c+9n6MX7bNz1yF0seOMCjqo7qv+HbAj+HMj7eSqzGXbs5Mj5R7HoHUf321bf1AJrn2fK5MnU1KT7vf7qptXQ7NshNPRYBWAJGccedzyL3jwXGmB66+NM/5+lwQtku2El1MaNovtU7eoktvk3VE9ZWHSsoaFhwPN9V3sanmgAyo/VJbde0u/xfmF/s4sWLeLzSz7PM53P8OJ/vYhqm4Bb+3JMqIhTEbd+377VIkWGjyJNnz2PRYuOLbr0gxuaWXT8VPdcF62b4TmY1P0SL9W9HYCj587i6BL9LztWDV6/AXj+TzBnoTe/hBKgmi8n4kzT7fdUeBT2QF9vF6lJx/IfJ88OjMUxxx3JomO8ezavbkPfKFAHof2NhCmpEZjj+z0bCIudbhshRAyoAzrt37OBe4BPSyl3DOrO4Qw/57/p+RhimKFTJGeqzwBQrpqtYRZAL1hlu4Eqtdj05Dj2VIYZQbT2Nlh7GwVfgpsxVFPSQ9+jTxHsjcVQHRt950548lpXpVcrrdyNbkWxx890a7LsbB/Ccpw+aAFTkm9wu/fC9W+Gh/8v0N40pbcspW0fNwGE6jn1bOTs35rU3fBM8CTAgXWwfDTaFfuvYPG/whZQDx3ZDp5qXDXwe/kg3eivQZwzgDZpLbhehxAaXb5QVc2QttOqDBzzUgkfQ0E3qDjiHvYZTwyku+VvMQLO5xf3dvO+a54gU+j/u3g+bUUIOgleUko+8OuVgXpPTm/8V+rNFV933d5uvnjHGn50XwlTsf1MM2nDdDIAy5hh723cyt3r+4lc3LcG1t0J//4q/P704G182z+fXM+H5swE4NFMIyfOn0tS6S5mWkAu9D6laaAz+nkMzwHHCiHmCyESwHm48q6LpVjOZYBzgRVSSimEmAjcD1wqpRz8VxcK63SkAN3nY+is6AmEemnbVvDGmBXFESszUIaugVEgYU+ApFrsrDbtxLlhMwYb/jyGQfsYnI9vwxI+2buGD8yZieJkP25aCst/iJGyrXbCIgZxsMfPm37/d9/AnXil4PhJEjHFcj5rWcsOmrUZzu4nA+2/+Y91fPXOFwFQ7BpIpoArptTzXD5oP83ZY6Kbhq8gcf90z4GU0iKY2tB9KJ958DN84dEvDultO69HGWbNmzCKau8reoDIGabsv7qwUxUg11MkZKW1LEKYASY8FIyEj+HH/97E1pY+NuwLfofhMFPne85rOX77wm9Z17KZ9lSB9j6PWfp9DA56SqyMlrGryu5oLbFYlk9okc52mTLxy43r+OHa//Z27Hzc8k84uOldcM9FJc8tN3J3970CwIu1eQw3mtF7T9l8kDGYduazOZrhqlJKHbgYeAjYDNwlpdwohLhcCHGO3eyPwGQhxHbg64AT0noxcAzwf0KIF+2/aQO5r26YXP/Yy8F9tuSr2RO+ORbjwbkbSE59wG0jll/GXmmFgaoAzeuhJ5gBbRgaKTPP7oRlD07ESmgM9oRQhkAqrl3+Mp++5dnAPn8pif40hgd3P8hT+58qe3yXaRELxc4UdnM60jahtZlkXEpAuv4F69jwkvU0w1pa9eXYeZzecQ/cthiuOsZbNitEZLa29LHHdpYq9lTMCIW7air5bCromHM0Bl3qgVDYqg13lOzLvmeuY2VlBSfOn8s5d1zOG694hJbOrpJtBwKnpLE2BNru9HYgfGEgbb7wyBf425a/kekIzn8hggRON2X/62c780yaRbWpUvbqgSYHcJg/9lO4rK5sOvhI+J6dCDc1VGV02UtNgd9JO/O7M9vGjS/dyBcf/RzgRViBP4/BO69UVFIipoCSI2/047QHhKN1HUDoKDga/G3nwF/+092/Jxbj2RKJtpLyjGF2wTKdrqxOIOz3pPv6lAslYJrmAd9iEUYkj0FKuUxKeZyU8mgp5U/sfT+QUi61t3NSyo9KKY+RUi6UUu609/9YSlktpXyD729Ay3I9s7OT3z4a/DBcjUEGCZySbHc6Srz1JVZIy/atSgE3vBWuPTHQXjc1bjE9IhIrYUqShsGS2mra1cFTimuXbysbdWKYwaiksMT1rce/xRce+cIB7yEcxmATBsOOeRa2xmCCRRB8jEEoBZ7f3Ulzz9BC7zTDZBLWWJ3VfotXJsCtiBp8lt6chm6HVAqbIrapVtvK0NTMSmtMNNPA9DHj5JZ7eHZXsQnsrK1/4H9mWDJGo25V323vPLCpbPkry/s9XhiUQciClFDPwLKlD2R5kVLybPOzbO3cSqbpxeBBJcQYDDNAxHKagW7q/PPlf1pZ7oavfTYYmZS2tRHzQFVIV/7K+l+GMBaZkqSE52+h5/l/kE0NbEyc6KowY4ipwTkSs++Vt4mlIyAGGYPVxnLG2lFJJRLcugpN1B5/GV1qQ4mH8sakSrPnVAmNwV9JoK9gfRdNqkpjzDP/fHDOTC48ojiCsEcRPFhd2onuXHd1ZQV62tIe/IyhLxtiDIZ++GQ+Z7XiACxnaIxwoTXDHmD7Q8gK68XEvAbce+8/3PaXLV0XKKQXV1JMpSsgqXbnu/nRlMl8+YgpRX17YH0T6xt7YP0S2B4iNFqO3RWf4OPqYwBsTCTYmEgwlW5uiv8KVesj58u9yPdXx0U6NuTiV27aEspT6b2cO3MGbV07rQO2xqAJYUvwfsag8cU71nDt8pfDlxsQZrQ9xbMVX7auZY9Vl6J4ts2QxtCT0dyPVtht2mLlGWIaAAAgAElEQVTWW6kVQdupozEUTCPwHnZkKjn/pmf6tz/bzFA4dmClfMzFNx7/hktQSuHlRLD+0EAS+eZ0PsLZs7/H5M6h+Sj8SGtpdFNHN3UybZsCx4QwwScb6qa0HO42fnTfRu7YdAeXPX0Z92y/JxjCGrJLO/4Lab+zbMHgsmWryGkGu/K7WPyvxWS0DEtra3n/7COQ4TLpNooYQ9du+PfXqPv353jpjm8HDpUbS4cxxEKMIR4SyuL2d5AuWITRYQKFEhqDjiBmj1UpjWFfygq0zKolzKsrr3Y3Jxf28ERlBS/s2s+aV4IaabdPcr9js6XZ/nTyJL4/dXLRJcOz98qpgqW1NcX3JlgyvLF3DRBcpKkzHXyXhmFgHC6Moa0vjzrvxsA+3dUYgsMsjUpr0jkShN1O9Y3U4hc+526rGCQ07xqnJdbwXMWXyXZ4tUlMW8VsUYNERjNMvvSXtZx93ZOw4gpYHewjfZZf/pyKe/n+k9/nvFkzOG/WDN6gbOdMdQ2x3nW80OL54LP9xb4/8G24fFJJMVOznblP55rZmkxw6c6/AqbLGHQEPHODTUxsiALp5ArWd7xYdL2BYE67txSpQLI9HuftR85myd5HrZ0+xmCaknzd3XRU/B3wfAyOxlBDkDGkbSLWk8sH7Kkt5gQMU7oZ16Xg1LMXTnaqmijb1pSmK92VwmdmBqW7gfhWNxYe5O7aGl7quf/AjQ+ArrxFfPRcD+lMsdY5T91DMmaN4S8f2sqnb7aYkS4V9nRmaM9ammOqkApWHg4xBsd/IdFh6wNc+tBf+WfbF/nJinu4p/MedvbsZEvnFr5fX0tjPI7+q+NK9jfsA2rv8cY22xvU4MLfrYNyuSrxMhpD2ibIzrvxJ/o5tZI04fkHSzGGii5LOJptWt/8v17Yx7zv3k82r8NW7z02sZsvz5jGn5QmvvvPlwLXaEl5z3fz+psB6FUVmtViwaQr9Cxt/cSLGj7GYGrWs27r8sJte7PB+aubXomggeKgZgyiMhj81B9jKBimqzEUbEJRbuwVYVDlm9EFxbpetsezcmm2RJ4PGYU3N/XyXuU5Xid2WsXJwtKnLVn9aloFS3d4PvpKe22I5elH+eHmq9z9uWwK/vEZaCkRHfGsw3SKX3lfwbpetf2K1+ebUat2uqYkTQDbHiKoMRRQJy+nURtiJIrf9opku+2jebrNlrp8BL0vr6NWvoKmWpKZxxist1ITmpp5V2MIvttW3cot0Q5QH3pJ4jLUvP2hqsFcgrCDtSdfHKUThmpLm/6R37Cvh+8seanI/OdIrrI/B0LrFti2HFNKlMpX6JWltbaunMUY8pkOMiWut0DZYdnHbWxrskxEJgLNkJ5vTCjoms/k6jAG04RcD2m7ntjs/Mvwt/PI9jQAsD+7g0TeYi4XPHiBe3peCPZ07ypaXjSsMWTz3j2V0LehGRo0Pm/5LLr3WDvX3k69bn134dpPYQ0iZh/OuCVvbCagl9AYhKcxlEpwk7Y5Ki41fvfi7/jx6ssAaOnu4yOzZvCbSXUAVJpWrs5jk7LMnxIMm25JBxlfRsugIehQrSXF/LOuQy1dYaGoX1JiBAQs6xnWt68HoNo06csHHebaYcUYUqFCUdLEsD8Uw37hR9pVUqVRaVX9tBlD3nZSJ8qIe4owqPSp2T02N/eviKXZUT9hZ2RzT45fJ6/lnuT/WmUdjNKMARkc+mphPU9O5siZ3rM1b7kHNt4De/qpG18iq3V71rqG3/Y4KdYcNCWBa2YBLBu1MMibKTdS44H1TWVXugtjB32cOH8uH505g/UJxS1fESthSurNagg1h7TfleNjaLXtr7UhW37O/ow00wiURU8XrP3+0sn5EvbuU5SXqXFKJ6hBZ1+4hMBAGEPCfjq/+eNTf1zNnc/vpaOouqnrfi5/wd+dCn/5T3RTUj3v92xVfl6yWXfeIvTrG9tJK8Wfr6LmiauKSzRj9vs2UdAME6PPctiqqRY0rYTGsOJy+PlcCjmL4M0qWNprwvAioBSjuNZYSlH44L3ncOnKS3l6RwdXP2xF/oUZg/S9OyNkl9elDuv+Zv3Yssxad2Dpxfwy/X2rfUj9KGIM9jj35XoD9w4KDfY+hMvc+/K6y8xNU/LnVbu8WlHA79f9nnzlM1SQx8z3sT2R4KaJFmMQeGOh1gaFt7Z00LTUnm2nIAR5RSEthFuxAQbOGEwZ1BicQJWUlkKVkmm6QSY0/3XbnHhYlN12CmA58BNAzX7h/93t2PgUy1Zvc85szGpbX0bKVDCQPn7e7ZTcKHgDXrDXfAhLgTndZOG8OVw8fSr2ScGL25NWyOBEqLI1hlc6g2rgVTtvsa/Tjw09zHyAy+ZYffVrT99O3uYyAocxVOERRaEUQBgINcOuDkvq+MVDW7l55c7y9waeaHyCJxqf4CnVYiBbkgm+Na3ONeFIU+djM2fwgi9suCergZJ1GYMTldTumpKC45p31+o1As757bFWquZfS84n/Xb1la5TL5wQ3pApKVxCIMwYSq2lnLQZgymBzf+GW8/mI4X7WaS8UJxb4RYrLNmtAPxELG/mi/riaAxSamRLaAyPz1+JSLQRUx2t2KRZVXnzUUcQl//G2PQvANTWLWglNIbWDUu4q7YGmbMy5xxhy1/SrdRXk7K/kScan+D8m57hNyuselRFPgbf2DjrprjPq2tQbX836TZXA50kLWaoh2o/GSHLQNw+3GdHWDmtX5N6xtJC2l72aQwQd7Q+CX12mO9dL2zlF+u/xCO7dhQ963nqY2RznhYgsaLoHPQawejGjpBDvzXT4lorOlWVZp8TOvwuRZnJYoY0BilNevI93Lz+ZuISKqQkbwbnq16qWusBcNAyhtYQY/j8w593tx2NYUHBGRDJTU/s5Cf3WTbATMzaX1UmLFQIM+AMWlVVyYnz59KT8Uo86D5ivLvH4PRbFnHmHe9x1binqirpUBR6w6WsHcksxBgq3WVGg5/dNrPHepr+Xm4ZZ2lWz6KbhusI7lUUhKsxWG3qFd/kFQWEMFHUDLvaU0gpaerJHrAY2zVrruHm9Tdj+Poek9I17e3JtrI5meCnviCLnkyhpMbQZktOidAtc761ev1S5xPTdqJWNNPhq/fT2VOGMbj1r4KmpDBjeG5PsM59KQ3CYQwSCXd+EnY9wQ9jf+bPiV+SLQQ1OOcTD0tsd69t5F1XNbBur9d3vz19SecSvvLYVwLnOBqDIvSSGgOAktxLzD4Ww2BX3Hboq0sw7D4oUqIHNAbruv+v1soj6bLLbThPIlwCL0smraVt6d3vuDdMWRSuGlgEy9ACZrecVmB/PsmPJ09ieddGVxiqIseDie/Q1NbB3s4MVz57JZc8egkF3zfYk++xQ7A9xuAw5JMzttN/90p3hmpC8L/xO3iLYjnwncikte2Po1buY0vVxsDzg/XOMz7GkBXCfW4oZlSdIcbQ3rPHZQwdqkJzzDNmD3RdZ83Qgz4GqfPVx74KQE4RVEqTrJkPzGnHWT2qeQxjhbDGsLZ1rbut26/fn9n8hyd2snyD9cHnY9YkEGVC8RR0nCTeowseQd6b2uNua75JednTOfrUDpqNFppTnnS96MjZfD4WIir2B2iGGINjSgrkFQA5IWiMxfqPR/dJEL4SMLzS+wqGNKiWkpiUpBThmZJsAuEPoxSq1YeJsTZ2taXpyWrkNLPsCld9OY2l615hV49lW/YzBhWvRHbCnma6z8rZnulDCNPTGISjMVjjEpSKJHnXf2QifVFnppO74os370qVrvcknHU4Yv2bkv69YTvStx6EQ4z9SNo5Ay/uKT4WdoS78SBCWBJzr2XO+fpd69jZnmb5Zq+Mt18qbtPb6MgG6005GoMQBhml9JeuKF5oZwyDCieMUwgvfl+aJTWGbptAFXTrudZWJNH9z0BpW/ULSWtM/f6aTEEv9rfYjEEDYmhoPg1iU3M3v39yE3dOqOVr6Y2BOf8aZS9/u+8BvrVkHXdsvoOGxgYKPi36wocudE1JqULQx5DBft+FtC9cVfBhdRV/T/wY8BzQznM60Vg9qte/S+N/I9/n0YAWUUVGKAgpUaUsWugqPG+au3e7AllLLMaeuMcYBlrGL69rrgMdrJUNn2/xMqsrTUljRZqFf1noXdu3Ps1AccgwBj8cxuCRXmtIZsy4nRvrJlCIO2Yga/+dtTX81Lf2gSJMl4gdV/Beme5btEc3S0vwvZkgUdqkmqzYs8LbYUsRNWqQYXwp5tRvKn59LyfiJc1FpeB/oZ25TjRpEJOSCaZJn6IglKApaZLi9SOmWtpOTjHY3paiyc5nyJcxuf1w6Ua+evfDGNKgYBQCRMHSGKx79NiZp/7PpjXdbT+twcbffwrF1ohytqSb8oWf6qbuy1ExA1JnXrW2M1qend07eXD3g3Sm/QWELNxYNwFhhzGi9K8xzEuvRPxslrtYUG+hON7e0Rg+fuMzRcf6jSRbeRVc/Rro3uPayJt8eSN+Qpk200Whsy6xEQZpUdourYggY0g6ocOqYs0lQDN1DK3ARTOm8vVpU+h0I5wcRmuZNFtjMd44fy4ZmXWPl5I8fzW5eO2QbMEoKr8uTZ19MZWT58/lofpcQBvd2tJNd6UlkU8TCaSe58r6iWy0+5wyYrSnfCU/fGOztWur6zPss53Pqq33Z6TtFC6kA1/XL+onunMybVdPdmiC878rHnyXhV6PMbQqFWQUQaWUxGWxxtATSjRr7tnrBqt8a9oUrvbRnIFqDAWjEBCanjOCJeQrSmhzulkcKHEgjEQRvVGHISV53aRc0KGTAa24kpr1f8PETjYwkWrdqcsjuLluAr+unxg4X8VwTUmTDb+zLMu6vd0cMbEiYEryQysR6viVx77C+gusqAFy3bSoKik1SGzbVYWfTq7HUIoZ3svJJGcaA1t6U0Vg2M/dneumYOjEJFRLizE4cMw8E4U3eVUljQEUFMHuzm6ae61xKqcxNHZlUZIWEbY0Bq9/MSnptP0FMVOAYmkMe/tMpJTs7bEkYVVovLblPkTd6wPXzvjChf0Lj+iYAVOSAy3VxuJnLgbgW1PPKDr+2/qJvDbdjZXyFoqUCTGG+bFdVqtCms//fQvHH7276HpJCtwU/xX/NN5WdCyvhU1JtmNTCthqr5ORbkNVBLppmescGD4mnDJSVBnVgWt12qaMKaKLJh+Di5ug2a9XUXS0qqcgfTJxdFdA2hP32utSI6fleLqy0upbejPXYidUqoKcnsL/gW2TuwHB1L4ttCDpz2FSrXZwRdXV3Lz6SSbVnY5fXNH0PF+bbr2Flyv1QMTQtpZuXplgRSC9Tq2hL9/NHXUTuLemhqf2NKJg0pvzM4YgIa6xtZP2nN/pK8k7gR6FVICp3VE3gXdnspCDtCuI2IzBXQI0+Gy5vGdKaheVZJQMVaYkJ4JOYYA+LSj8NaeaXVOSg6RpkleUouqn5UZ329a7ebzWoxEtBO9RmjEMvvbaQakxhJ1QYUibrJcrkpdTrQ+xKaYWMQWwfAyOyjfZ96GauS7uvOFyPvTrlW5ssHWC96IMvf/V4TrSrbxn7iy2hxKlfjtpIo9UV5GuLM7O3ZpIgqFx7XIv6qFcbX/NR/S68l20Z/LEkFSZkk5f5IOjMTiMISZBUT0C1Z72MqCdAn9hVCVU1Ao/Y/DuHZeSm+3IDdX1D8D/rcqy/F9/5sXnH7Qa2qYzNRWU8k2fTcxv6tGliSwx0bWcZ3Jp3f5A0XEA07DD+ELnhxlDxk5w6c5qLN/cQteOFYRRQYF3K2tZGFvD1ZMmogFrk0n2xGLkdIO71zZy8V9t86b0/XMECiVeUmPwr1GeNtM096UDmd3dNtHThQg4LP0EIVX1AExZQqxuLWogT9xDwTTcshcArTbzda6YI2hec0wrcTNzwCIw6rG/4oezDR7Yt4w17Y+jYrC74hOw8mpa8u3stDWAiYYMONt3tXeRs/1/pjTR7LwTZ/7cl/xfztdu854h5HdzrtRWsMZLYgV1JBxnbKazKPHklViMyrl/4P49f7XPCTIGB46PJefzN3UoSTJCUC1NYhRrDCmtDwzvO2/PdXrRgDZm6768ogHgpxuDC4PpIWZUUSLnQ5eDNyUdlBqDFTZaXnp2uHLYlOTAsCNz/BKUHwqGS+T8GkNi1xJeX6/Q0lGHbtS7++cccSuONVEvU8FzxW1nctTRZ7EzW2zmAC/yqRReTsTA0Djx6S+CXbV5e2uK1/na3FNTza54PLCua2e2C920TEkJKQP3cGLg64TFyKpNkx7VV4Ct0ExTt0UwyzmfDVOiJC17eW8uGyBB/mluuCGTFs5c91XilZVcwlQ3SirMxP0fpp8xaNJgZ3uJ2lW+1axeiZWe1ortY5CG9Rne+dwetjT3ceqJoagke5waOyymeVTnvyEkQFSJPIqQPDt5P6vqJjDRNLjGNg28defv2LvjPWza32vb2G3bd173ggik6Zp7mntyroi2N+0lSUkkQuis29vNwvnWfOuyE9Q0ARlFoEhLqp1k6vTZTnXTNg8KoaMKnR9MKc601dItTN/8VZg7C4C8odHWl3clxWyo7pIjOc/tfIbnJ0ygP41B2sxeBzJ6hmqHyTx6OffUf8OqrQzERS7g59jT0c30qXZYsjTJ2yahmG9unM5z3GKVgAw4n8GbX+0+SX26miJpWu83191URDUumzqZGLt4uGkXrb1fcisNy5Cvz4l4ymqeht0lEmQUhSpTkhHFC2xl9F7ieiWaaj1ju95TZDKaoRvsSAzclLQ35I0wpAy8ispA5WGdmBJzLQ2HvI9BM2Uw/j6Eghti50VSDAYKumtKmuSTaP5ZW8OPp9QzIbknoDEcmdzsbht6acbwFdnM2dv/zEbdc0jV+q7drZZ/FftiKj1ahoUx7z7hrM8fTJ3MnyZOYIruscOtbU0UTIOYtCT4rUlPerllYh2/qJ/IBLty7ISQeWai2M+OdkuiLGdKak8VXFNSVs+T9WkW/hF3DHOOn2BfTKXPLWdQ+trSpzFkDY9wx2WazXf9oKh9Z69XUO0VH8M/PeOdm7Mjxtr7Mkgp+X3DDpatbyIbivvutRnDfpsx9ISYtgnUYJ0TVyyid43PXvxk291s2N+OKS2npjMPG7tzdBt5Hqyu4srNtzJHaecjykq3kifAktYfBR9MmIHSD46PIaUoSAGVhtW36hKSojQT6IlOtvje+//rtM4vdL8ScF4XTJ3/+P0qHCqTUoLvxS/RSnkgncGCgSSnZ0jiRQee1Xune/yZqiTq4967NI2cK7QVMMjZ31LMN5v8PrHw2sYOce0znKxtmFuZdTWGHbt2FmkCfvz4/s2uRuGff9a17f++73t70spHqDJNYkjMUCmerNFHpe6NfYtZnF8zwfYp3VZXiw7cVVvD2uTAF7GUwqTSn4rks/85GpVjSjrk8xgq0/uKCob54Th4DNOSHAdb9kxRLFIWk5I6H8HcaH9gH4kvRxY8Au8n6qZR/PL9eMn0Jtbxvoin7jJrS083rP3rUu2BZDpZRmOabKgszOY4qqCxvmMda2p1WtQ4fbK47sqy6mr2VVpS2Un5oFq+OL6c619+Jwm0QK0ZP7pynSjxPqSZAKEHPia/bdZhDCagVu3krDmz+FutvZ61LZmFbbn+O+Zsp3/ClAhh8D4ZLN8N8NgOz8z2ii/ao94fNy8KbErEaTYKbG9NsbsjQ143i/MY7Pe5aZ9ltmmNVTJN15lqq/0GXhTZBEoHQVTO/gtqzWYr2c0mNiaCsyeYfGvaFO5oXM73Yj/kmsTv3USrkhCGWyzOMA167BpGTkJUhWE9a+mqrwqpmmDo7sf6+qgyTToTla4mkTQEuxMqzYXnXc2tLyR4+G3gAwl7jEuJISVZPU1S5Pn21Mk8XZFkruJpzJoQXNvplVFR0JF2B/LSJGdr3zGf+SemePM0GzLbPllVGfgtgSPjPVTYjvMpoqdfEbGuMu5jHMGWprAyOZy1Xk7O5XiqLk1TLEaVHfUnQ8U7c2aKU6U3/roovnuNPT+bYzH+OHECV0yp54JQ2RU//qMvKHhKARW++dOdmOdu53c9bt1X6m7bgeKgZAxxPe2WdigF5yMxHGNSiRdSYpcLBY8xVPltt7bkmFCyzHn5Fnd/p4+oG77kkoklpLj1itfvubqnepbTGGbZH/7mbHdAastppRmjISQKkkmGQadmVV7MqrBJzi1q2xFTaUkUmKtpbpa4gyPiVrJOLRkKifU0p5th1xPw4xnw1G/Z2bOTXqtILkZ2lpX74RtUw9dX3X5XUkgmJXZb42CXGpaKwZ/qaotsrAEfg21SqJEmepm1a7WE53A0fWq5XyvLKvDxWUfw+dmVPLzJChHNaybZkPlvTzzOx2dOp7HDuma3ojLRMPlUr2XC0oQgofTy8/pJbE14TCjpY0JKRSOTZ97K7DvOcJ/sXcoLdPuk9JU1Or+on8is2UG7sR9CGDgVLnoKPS7hcmzVCcPSjgyES2QcfCd+O701waSrOtMyK95XXck6+x0kbQGqYs7tlNOunStL+jPiepBYGmLOzFIhMjxQU81FR0x3F79ysNsnD6lCx7AZQ0rTydlMMCatsO2sEAHnrcMYjsuXjhCUQvBOuZq0kubi6VOpEH39EsdptUmXMIQ1BrCe20kWu7irh4Jisi8eszQGCc09ffzj+b2QT2GuvAZNpplRIv/Iz+gqfdurKr2SGqXo04/aOvhcd3F4tN8MGxPeNfJ3ftLqtzxMTEmKWeD2RPn1YZ3J4zKGEtXNa83yKeiKsBiDihUXHEZWUTB9jia/dJXPe0SmjuJ7+BNiZvgYQ28ZH0OVHVHRqeUCjqusL2v0Fz77t4lElTApRCT8auTZfZ7TsSUuqTVNqkLPefmUyWxJxPlp/CbEjD/xsfvOh1vPBj1L06M/ZPG/FqMeYTFHI2sxnbxvhvolzIJNSrpUlbdP+FfRM15dP6koKsPfm5wdl15rWkEBpSTW3kRpE16tbxycqKy0Knh4oyW55nQjsLDJBNuntCmZZHeX1SatWGPk2Lp1Ae21+/lLXW3APDffjqRSUvM4QryHrAqx3t3u0yTQmWhYPp/X5grcX1PF7XUT6Krtf53tSb2WNtRt58BU+54pZibcPj22Zx/1PtVBj6fpq/Ac19+zV+iLSxlwWqqGZ7ooRxDyvvk5EALj1O3NGxkqhPduekICUIXwGKsqNHRbg8wahutb6lBVzpg7m3NnzXDzbwBE2hJMJvcTrXd6fhXLJ3XxeFUl/5pQxa5kec6gGaZrJvPnCrwhZ2mFmhCYtsbwmnyBWs0iwtVSoiJRhM63lrwEDT+j77EfAZLpukFSq+J1fV5253SfydVfyHNbonxxR4APptNUlDDjKUi+2dHFTU0tKKrHGApCQNO6wIqWA8WIMAYhxFlCiK1CiO1CiO+WOJ4UQtxpH18thJjnO3apvX+rEOJ94XPLYabaUvZYt5sk5ZPmfE6bBElisvyjq7bzOSZlwJnjICtEWWdRKucR3SqltHPbwXxfSGa568WkQEjo0fMB4pn31Zm5vW6Cuy2RKMCkogVGvGnhd6jvTMSpMWXJMLeGqkreoVj1hbry7YH9DmbrFUjdYkw5H2PwS3aazzD0SJka8wGNQYY0Bpsx1JimtRJVifONZOm1Fqp878+fabqusZO6yduITVpJ2pehOtU3Nh2xbQg1xUtJhXj1THQZd/sq1WKT4Xxb60pIwYlHzAAsLdMp0iaFZeb8RG8fn+3pcecpWHOqXG7721Z9AtLtbmVV//tz+EBCSlKyjqmmNy9+N2liwI56tNu/0A0M/zs5sL0hLPUDqKGcClMIpICCmSWpeFr0ipC5x88YTlB2uVqnoeCakvpUhYIi2BOPB+bVMTt/BwQjB8M4c85kHqu1vvWrSuRa+JHVC1T12ZWNfY94kh0HbIDLGGJAzI44qjJN4tLKRgeQ+T56bSvCJNOkYscXOa77CPd6foHQr/+m+glAAUtzKnp3dlcv6O3jLbk8hBnDH97OkU1WlN6oMgYhhApcD7wfWACcL4RYEGp2IdAlpTwGuAa40j53AdZSoK8FzgJ+Z1+vX+yMx9lWJqLID9OnMSR8jKFWretXpZxcraKLfjSGfhhDwq7DNFfTmBKrLtnGwfyCxqJ0hsp+1qc0paQayEg9oDHs60mxN6bSJwR1PkJhAqqUTAxdU/ER2nrTT1gs51llCcaQF8KNjfdjRbX3cZ+Y66DKdsQXfIzBX3U2NwDjQ2Pcb5Lx1sWTvfvZ02kxpRpT2gykeOxbY7FARVwH/u6vq/AkMqFmSU5bRqJ+JWlfOY0pPiLTMeNJqub/BikE7T06V2sfAywTiSyRbzLTpuxvUTZyvGkJLn2KguEkFSLIKgrVpmSeFoxgaYqptMdKT31dAKkWfvD4Ndb9fc8/OWuF+GSNOlJKnZvMVgrOWLwtm+W1FYvc/ZpZWbJ9OZTS2OoTdSXbFswMCcVjomHGUJfyggY+mXjIrYllCEk+XE4GKPju3Wdr31PKhFODpaUOFLlcK3P23Fu0v1K11lzRhcCw53pMShTDIsKVto9BsX1pORl3AxZUI06jnEpW97T6I32MYTAEWAW37Icf/muovrVG3GoB9v/R1hgWAtullDul5X35OxBeVX0xcKu9vQR4t7CK4ywG/i6lzEspdwHb7ev1i6wiXBt1f9BsCQ8hSQhvktWqtf2eVxkHTVovv5QknVUUiiPpLdRXQYVpcn9jEzGl/z7GkczXdLL9SAoGUIVCXugBqfrXKzbwgTmz+Na0Ka4UNcmwilIoFBcI9POx8LEaU5ZkTtsSCfI+05fEWnjn+QpPKqkzTS6L/QWAgi/Ez19cTOvPoWMjEBZsKphCsn/fc4irT2DrKiuSpdY00UW5OCaYo5V7KxbW+vo9q2Y1WZqoUfroy3qmpLdmgpqAErfDE2WMvLSImo7AUIPy/U/aOkC3iGNGUThm78MAfDULTMUAACAASURBVGDOTJ6aaGmRjimr2jTd+HUH+2MxWm0iZqaODhzTEOxt28CerLXcqWOO/PTeGUw1rHsmTQWpxovm61ldU71nkfC5wjfobT6XecoH3P156c3TgRAPPxn+WWs7d+1rYnJFaWlcYrJvmleyoS0USuxn5ilFuP4hDUlOL9bKcr5vpceWyieXWn5zCJjb+UCRRvuHplYKiiXgGQI38igGmNISNKrtqCRV6CwUm+nKGq7JrF2fAQjSvhWLZwY0htIot79UReiAj8HHGFyTulPMcjBrjksph/UHnAvc7Pv9X8B1oTYbgNm+3zuAKcB1wKd8+/8InDuAe8roL/qL/qK/6G/Qf88PhK6PRIJbKTYkB9hmIOdaFxDiIuCiwXUtQoQIESIMFiPBGBqBOb7fs4FwmIXTplEIEcPKfewc4LkASClvBG4EqJxfKc//zmyeq6wo1dTFzbsSfG5+gXzbu0lOfdTd/7UF13HT+q+SUkubHiaZSV6XSdGYgKX7mjhxvhV1s37XHk47cjbn9KWZqetlnVkzdJ1t267iA2/8MytzW8r276+N8GR1t+UkLINzW2t4ZYpOupDiop4uvuqs8yDjIDTelsmy0rbb1pgm1YbKsXqc7dkv0lz/G/c6Sn4+ZnIXAH/b18z5s2a4x77S2c0xBY1LZljX/vnp13DSnR/jA3NmBfoipYIQJscUCvQoCm2xGJdWvZ5JFUfx7c57SvZ/XkFjd+LA/qBrWtr4mv1sR6Qn0lQdDMtTpOQTPQX+Vpfgn/ua+PDsmUXXOKHzCDbXNwX2fa2ziyWtl/LRaT8LFC0DWJDPsymZ5KRcnqSU3NLcigROml8c2rvQnMbJC/6HG7Zcxm17u7hs6kRmmTmuzdeR6N7OJvNIvlL7Q5qnXcas5Eksa3yON09OUChRAfVPTS2ckvN8FO+fPdP1sagSbnrnw3y24b3u8S919fB7e8WwX7S2M71vJicr2/mT/j5q3vNtjnn8PL6R+w53zL6HLyd2BMqt+N/1z2Z+josfPQaAmZMEfTO+A0Ch680kJj0HwBxNsjde3uRwSWc3N0+c4Jo/nRpgv3zul9y26bay5wGcks3xvP3NXjzz+1y3/yeB4+/qlayYIJgiY3SgcVzH0WydElwL5BsdXW7BPmduHb37g+yYV3rZ1F+dfDffWPsf7u+jChrvVV/PDeomTstmaaw/mb1ZK3H01GyO1T6aMkfTWNbYxCcqz2H9DGu529MzWV6qSPL0K42cV/E7/p77HwC+NH2qm0fxh9RpfKHGWljrnL6v8ZOLP8vnb3ueZ+R/A3B/Czwc7+bX9ROZ372QXROfBeCkrM5LldY8mFUQ7LM9zde+9mqm7d/EiWutZMATQ/NzZkHy0D4rX+L6136WGzLWOvM/buvgpokTeEs2x50TLPP5hs9sKDlOYYyEj+E54FghxHwhRALLmbw01GYpcIG9fS6wQlo2oaXAeXbU0nzgWODZgdy0XHinH4adOn+CeMXdN6/5ZI6f9Np+6710KXlWV8UCGZcOZurwTEUdD5WJrgHojlmTy+gn8gmgR6sMhKuFcWNTC6f2VpLUBH2qJOe3EdolC1I24akwrYIUVr06QX18QuBaTqr/z4/7RtFTzdR1sidaq2QpMsHps05npm5wVioduoZXzjxr+xDq4rXoc99d9hkGav8N2E7N4rC9pJSYVGAIEXC++vHaaZ4d14kVF0BBxEvaZt9kE+eXKpKu810oMffcT/jWJ56QMHnNERYDz6sJsqpVsdZZRvNOYxFfeNep3PCeG/j7h69H+fDvOLu92JeV331RgCm0i3rSBS+hqcKo5M1HWhEsTl7EqsoKqk2TO/c18f50ho3mPADOmJOkYtJMPpL9NccueCOpuhPc9+LAn9uQjHnj+tfPFRcaHAikoOT4T64sLrsRxnTfXIjHit/xnoTV93miAikE2XhxCHKL4n137qpnWukgj9S2S6mpCgpdUwyDk+QUTsnmuLSji+q45zRfHRI0naoHfaZ3/Zwi3PnR6wvz9ecmXDfxzQAsSFWSSVoCjL8mVLxiKv/V28upHVN5ucNzx/pH1T9bFUWBfmNyvDNjvvWk1ycTvBKPu0xhMBg2Y5BS6sDFwEPAZuAuKeVGIcTlQohz7GZ/BCYLIbYDXwe+a5+7EbgL2AQ8CHxZyjKLJIQQjocuBVPEUaTkPeoad58h41TEVfpLjQerwqgTt/7x4z/OV3LW/Wab1exMmmxOJjg5Vxw1AZCzXdOFfiJEAPqM6n5fwDTDoL4qgZpTaVFjJevvOw7NSiltI6JEQTAlGcx0dhhDXE1QHQrBnadpTJh4En2bf8ybuIHaZAUq8Mu2jpIRH135/yBlj/9rqqZTUTGtqI2DKSUYQ0xK1p95O2cnvBC+RNUUd9sswRgqpcRQrA83XIjMwXFTPALr/1Dj8Qq3uJjq+4j8zuoKh4Ae/wFe2L2X9bv28MG0jzFWTyZuhx9XVFeRUe0ciTlWrMSPvvBJzn3TbM6YdQYTKybCMe/h9uzHixKVCtn5gd9pdQKq5n248YSlpb3wqbX8rMMK032pIonUTmSBXQJ+f9K6RtJIuYvmxFQFZd5pZENzpMb3rhWhcMm7juETp85lbr2PWPg6WSpO4ASCQlCpqKTFRy/mw7O/wjk9BS7qKr006mt8iWjJEoxhe4VEFSqfSFgaTp+voGRMt4jwPuEJPE7+kCnLWA6kQk0yqK12y1q2zfoUf2puZb6mU2MzhmpjMhOMKYG29fbc7TO9bykrFJcudBveff1ke71uaepr0+8kYUeaGabkJFsgUGumkZRwaud0cgXvTOELlfR/oapQqEj2p3Vb5+VlnLiPMZRbunggGJE8BinlMinlcVLKo6WUP7H3/UBKudTezkkpPyqlPEZKuVBKudN37k/s846XUpYui1kC5TQG4RsMU8QREAhNLQhJRVwZUHq4Q1z+9y3/y+cK1os5M1tHTWYa17a08a50ufIXdmTFAUrK9Moat3LkhBIEVAEmTapHyVdRUARNJYrDuYzBlG5SkQCmViSsUhXAso8sc6OSTGIcpelc3dzO26Za4ZfzNB3UShzLYkxVOCP3a8CKnAL4eN2HAdD7XsPOfWfw+l7rY5lTPY2qeLHEVmGaqFIyswRj0cwkzHwD36g9wWv/tm+525Nqist3JKXEtKXF0uUfYHq1J7U60TmKhHgi7oZxVqK4H6Aa84ijG657wjnkP/uYtekjZMaEmcTsmHsjnqRPCCYYJpz6Rfif1TD31KL+GJljOH/XSYF9D3/tHdaxedb/I/XdxHSvH6+bYUmYMTVOb9IzfcW1k93tjrjVZqKSdSsNJ1SFY077MGlbYzix5Wwu7O4JxPgLJcY33ns8P/3Iie7CSBCMpQ8nGgLMCUXxlZrWkysnc1Ld+7iio6UoudLBsXUnutvJeOlkrtdPfT0LE9ZzdyW88Y/rlpkmZ3Ouet/3ostg9N/EpKUlXHveG6hKBCXtTeZ89CpPkKm1GUO9fgH7Xv5moK3zHDLpaRU5IYgh2fq239JnesTaqXRb1TufhGGbYM0KKuzbX774ddzU3MpDe/cRs7UYI0R+yy3nqQiFY6ZbfdB81n/VpxWfnf8xb89fE2AM4RLfg8FBmfkMVkjg/2/vzMPbqM7F/X7a5TVxnNiJncTZV7InrAFDGhIg7E0JoTRsZS+lG/vWArdA6Q5tgVsKv0sJXSjLpRdoKBgIhUKgFAiUhjQJBLI6EOwkjm3p/P6YGWlGGsmSLdmyc97n8WNpZqT5NHPmfOdbznfcKLHNO4h6TMVg29/mgbDfmzRJxy1d0+FKMlPjAp8HqfjwRA7b424tHM0ELp/4AACtLnMg7HxGvBTEMc3JZrN39Hx8x9yGtBmNYp3L3A1rUsyuaKlRz0WM9ZP7hwTVXgTt/RhaNhR/1BhNB8zUu/l7dnNA/9NZtf5DipQi4nOOuvaaxbisUcd+/Uaxa92FtGw+CRC+sKWWf677EJ8Hil0UwwMbWvjDx5sZ1p5cusMrxncPEH8sLzswNJ6lXFaU/H3BqAJzXkiqBl9uc2dM2DGcJZ838aWmZsJ+X0xRhILlDP3gNJ788BOK55wdOz5CCcw5FyYeR7DEeHD9wEPHPATAvGHzKPIbimm710dUxCiA5g3CoPGu8jz37cM5c8Exjm1jq0rhmm14v/IojJrHb4dchV/iI/Iif/w+DCwx6ufu3V5Pcdv+HLr3x5zd+i3OW2AoiTJvW8xF4fMIeH0cO34xANfPO4JLP93paOX2HHcn8XbqpnTL/HH5DHel+/VfNGUIHlF4UoxUPzvo1thru2I4zeaym1s7l3JvmIEJhRsD7YYML5cbymKazR0XS0s3uW/hfSwdv5RjJ48lkDCPYXhFKV85sC723lIMljVYZUsltZTPoIq4ItntEbwKxh22xOEeskratDbtB2YKsYqECPmMazWispii0x9lyJyLCBUbVo+9GoGK+hwWg33g6vV6EfPeeUoG8seNm3is3xGxTxepVt5WI9lCBQHbeuZua4JnSq9VDIkURRVvr/swNsIFUJKsGNolSsjvTRr11LQnB6Id/n/Txze6ppK2YOoZlAM8xfQLGiZpWwem3E5VEpsI5jbD2nPARVQMqKI1aozYtrpM1rHW/fWbtkcUhRehIuRBRUoQ86E5c9y32f3hmUytHhP77JbPWwiaIkbMGZNWU9prxmf8XmM0VjRoNNGWYah2o1HvIYgH8LTtwu/14U9Qgne1fpExbW20tSeP/geWmB2/ihceCHlDjCofabzxJrsGQkrFpvunUgyloXgp9Eh7CfWfjSasFBX9+vHAsNsACAeKWd0+mRN2/RxvdXwEi6cEjv6BseynNWNdvEyqnMSrp73K8aOPjymGzWZcwVAMqU38EZXFVI+IF0c/xay1hC9grL95+p9YuPRS5k2ojR1jn0EcHXEOP5mzgj8tuQmPePhQVbG77khGTTkEpp+OnPCL2AppVqzj6v2v5uVTX2bc4GSfvyeVYhC7Yki+tsWBEtf9+ycMjsLm6DxVpxK2Laka9MdfHzfuvNjruTVzweNlYqtzAmGkLT5q90cVh+yxrWmM8x6M6jeKK/e/Eo948Hqdv2dcVTkhf/wal/iNQcB/thq/5ZGN8eQFK8Ywqy7uotwtHmMwIx7OOjjuFrSuy86W0bTHKhwLIfslH1kPR95IkakYZtQZ/UTT+zfQ/O9rHRaDw5Xk8cQGpt6SgYxra2NkuDQ2f8Fj6+H8Nq9CuvlRHdFnFIPfdHzafaTKE0CUcwp/NBpiYGkwKcIw2MXl4WYxjBw8kL9cfVLSsRYBCcQWYGnvQDHsGHxIrJMrcjnW6/ER9HtojhiNd2vCzNhBNmUWwFB2EcCjhP5BQbWXIKaZfd7c8bx35aUMKI2P/tZvj1spEa9hqlvPfRNhHmifR121MTqt7B+fKAWw2zLfW3fj9QihBPH/FD2UupYH2RN1zoid0lrFRdMuMt7YFEPAG2D5oodYuWQl+N1dST5TSaVSDGXFcRmV8nJr0bfgrKe5fdnhnH3wWADCvjDFAS/bKafVtjxrwGNTRlZnb97zsM/M+jLl2mQ+NYZiSF/fBpvyuabx06TdA0qC9C+OW0iS8NvmTahmck05Vujg4iNGg9cHx98BA8dx0vRaFs+s5VtHjgOMCU4lgRJXubwplZhdMSS3w2JbvMq69l/Z+Tl3bNmWdCynPIBnjntWudfWUYVsFkP76KP5duUBfH3qhYyrGAceH2NbnRMIG9sN94yKBPn5h3CATSktnDqUH9cbrk9P0yGOz/kTYi7ehCrGQ4vHUFVURfMuY/BVansOrQoBQ/rFf/9uj+FKQjxctjBuKR6waRznb/YTbR1EW7s1sIgQ9Lq0VXOAMarKjJdEQ6CCDjPBfhe89uBzrI1LzA1tP4Pfm+ze6gy9cqEeNwJKsb56AYr4mgXK44/5TyvahB1+hefTmfi9nqTMiiEdWQxWg/KFwB8Gf3GsNk5tpISNXiNQOMJXFXsA2pWkLT2zK1zD6qrTofEx19IbHvES9HnYbZqm2xMshur2CFvNEUJA+UxXkkJEKPaD2nEMRSVm4xEh4BOwjRrf3xI34VVSXSfhVt95vDzvYOZvfIFp1WMxJqbDMVMG8/Y75mipcixeSeUdhX+3jQLiJbGHtZdz4pgTzZNGY7Myg94gYV+YsC9M0F+R9D0hpfD6wxAh5azzMpvveFBpCf0HDYFhUykGwmY6aNgX5m9XzqM9EuXZT+LJcyG7K81mMdixXGaNu7ZASbHhnvJ3UE5ChKGlQ/mo6aOUhwRs7qNExWD7GuNYn3MsFw54+cHiqckfcKnT5UkxgmzbOR1/uZGOaS858eMt23iuKExxbTzg22re6aFt7bzSPoX6xC+bcCyef++F//wx6Tzlwfj3BGwWQyBYyrJj7rEJ6mNcQsXUAaGBNAMqUoQv4qXWlqNy+dH7QdkQrhjwUxae7My28iYoBp84u7za4lE8s/gZXp7RyAOvbIA18X39i6qgrdHh9oqImZSScJ9+sfs82A1lIR9Nm48iGBXamybFYgwOTAtsQEmiZWz3b8T7A4/HA5WTYPA0qJoIa/8KtmfOPvM54M+NYugzFkNQKbz+oDPlyxt3JUUFTm5q5v5zjKAfCSs01bqUU3DUJbE6CasjKBrAoXta+OO8uzm8zcgrvmlbI4N8/fGZo4SOXEn+YBCFVdjMeezZn+1kULgSEWFPxBjNtIvQ3xrZK+VI/wuaDd4oiSGICIOL6ggrZxaMXTGs3RavZ58o6YuXHc6Llx1OSaCEo0cejYhwxVHjufjw0dz+xal888KLefELj8HUJSk7M4AVbc4HtchW2BCb+8zuGy3xJ7vqgkoR9KXPSgrbYgyz6ir54ZfiHebeiOGaKPIVUR72M6AkSF1ZXWy/I05idaAJrhfLlWRlxHmVMlxPHfDY8Y+xav2HKff7bErJk+KR9Ji/2eeSmeaKFYS0BZlTuZIiu8Yxe7PhYrRf2y/s3sPN23cQtnXolsXwXPtMzmv7RgpZk39D03u3UBKIX2N7kDQUSFCuHh/jWp2xqUmDjOlOpcEidqhSBGJZPpZ1VFPiYUCJ8374ErIXEy0G67oeOGoAi2fV8sW98YWD+i/+H7hsHYEES8uwGMzrdPqjcOLdsX0DSoKoSCktmxaD8hP0udyvgNGOxONjbJXNOraltzssBvHCgFFw3vMQMtNvJd5S7J6NkM+mGDJtKy70GcUQUgpfsNipGDzxrKQ2UfiVYnTM9+rsCge7WQz2N3aLAcDMgBnXfwwlZs0Ur1JGHrx5QyIpFMMTH33Cgx9vJuAPoMzy3b9uj+czn//pTiNwaJ5zt80dc+RoIwO4fzTqCJhbiiFizmMAqC4L4U8cJdoeDLt4toRFAIZWFNGvyOmOOP+wUXx7wTjCAS9Tavsx95B6EMHrcV8jAaAxGvfPXtG4g6ltttRWmwBBb/yBDnuTg8/vRUbiDRvXIVUBQwnGs2d8Hqfs4yoMV8vpE0+PbZtdPZsRHqNTCnhtKZlWBzrYORL3e/wEvcFYgTQfxNtDGvxefyyW40bAFtxNbTFk+ZBbitahGFLHQ5RLiXiLIltM7SPT8tqpymIJComksh/tSs1jGwgEgwlzgjxehra34zPdw0oJx+5n1I+qKS+Lxfju27SFv6//KEmBJ55z17qvEW01rFArhnNz5a2cuPe7joF/0OdllYq7hyrCleD1EUpI+rAvNcqow2HqKbG3pSGnLO4Wg9m+RXj8Yrvryz347LT0VOxYjy0rqba/0Y7t1k2Li4LOlD6jGMIqgj9UTLPENbDyGhZEFCPbIugY4Tmf1BozxuBVKua7t+fCxxpfzGIwc57FQzFG5xBUCjy+mPmaKsYwrL2d/VpbCQYCRE2LYX00PpP3ws/MPHCz1e5S8Y5y0chFgJExYW+uITFLQovEFMNXDx3B+fUjnSdP8xB1Fm+KUtgA2HK9FzbvjskGgIoywLzudovB7yLjJ9EqQua9S8ycuX3LNi74dKfDrVNd7kyxrAxX8vayt5lbO9exfYjpTfXbFUOwFJY9AUt+myRHsb+YneYI0qeUQ9F2FrtfOHXKovG/g0Q32wcsd1j8EU+VldSvyE/UNlqdtGMITe/dEnsfDscnib1qTgKbUjuQhy84MIWs7t1KcTB+fp99IOBPsLo8PrxAzY5xqKgfFSnG7zPaSWmwiOf9xj30Y8bm0sR5fF4h2lJD+27jObCKzK0OTuMfaozjegf9Trn7m4UBg0kWQ2qKAwmKwc1isKxT8ToC4UUR97bkUAwxvRC3GHaqEn5/3oH85JRpjtiNthgwsnr84RLasFWKtLmS2sV011gNMiHI1i8a4aDde/jplm2UmyNxx22yGrvNlWRtr1Zl/NfW7czd04LyePHFYgzuslq368sHjqTYb3Rg0XbbqLF/nfHCXOy8iXiHN7a/EUStNBd8if1+22jQZ57hiPFVnDg9nvECJPme6/f+kPeP/kNs8N4Zt6QIJPZnQdMX7rH56b3ALo+tw1ZRHti0me8PO9bRmVQX1RmfbR0e2xby+fGY7odHbPMc/B4/C3bvMZSpTfhAR0FhE2uhdL8nYeQ/Yi6Ek0uVlPhL+CxkPNi+TDvpDvDbOslUnerUWkOWfkUdlxcB4gF02/X3pShB/fer5jFlWHxyV1vUeS2KQskZTnNGVjNzeHIsCNx/w/KvHsDAUttMYbsrKTEN2+y8P9t+NLvXXUzLppOpMoOuh9QcwurgNEa32MpvpFMMVqdqzumxLAa39h5MiN9YcackxeBy3ycONtxtJQkWQ9DVYjCf9YTrtLCxjGGxlRTjJ/E5FEM0Jri1da0awpB+YU6YXoPPJutubTEYrqRgUSn2Hko8fjzKGEVHBYJK4r5XE2uSiE/BXVu2cVj52NjC6k6LwXIlmZ10cdxi8Hq8HLtrtxGMlLjFkMqVZDGmuozFI86nZdOJRHaNje+wXBhmq20hwFXbd/CLzVtjo8sBkagjLhG2NYhAGreA3WIYUh5ivRqMDI+P/DozxvB6ki2GQ8caD3LQlkl1Zes5/KP40PhBKkpte4RFFc5JYDVFI2hecyXB5ni9oKP3q8FnKrW3zJLrgjiDxjZ8LjNr3YiaQUxvByXSLcoCZewyy0G7lUzpDKHieAebSjFcdfQEHr3oYEYNTM7YcsXTcYxh0oBJ1NfWE/R5HR1K4voM4bAz5rMgWsH0CV9KfWqX33DgKKdysVtJSXGTMfNh9ldpDg0h2lpFpHkCo/uP4smTnuTsyWfj8wrt9nF7GqvNehatyZ5WjMGqfJBOMViEbaPwomiUIS4LAz18wUH849r5lNisot+de4B7TMhyJSXGO5SPZeacjqSSGDHiriQr3qOU3SrUWUkOgkoRDJfgUAymErBqDPldGqxXGX752EN+/osU/WYm0Oq8OLHgs9kRlQ81/Mu+IDWlPjDTn7cMPIiR3szSVQFKgsW0fZYwa/aEX8LUpVBhuYGEU5uMQLESHx4Mi8FSakqJoRjM9hpIp+9tjXH84DI+2dniMPE7Q2KM4ai6o7jhwGm8uGY7P17xb6xVh5+MHsgp4jb6ccrr8QiqvRwf8ZTEsC/g6LwAvHgIe8Pw1efgsw2OfR2thWHRXjYEmtaxN1zT8cFAmS0Qm67OVTYUBTqWNeDzMG1o6mKLSVgu06mnQKNRVC1RMTy06KHYa/v6WK3KqRiKQk7L4Ji6GxjQPyGpwYZdMexffBznHXJC0jH2IHBi5hBD58DQOZwUfZd7X1oHGJ12ediwfn2JcbNMOkBzlnKSxWB3JaVYKCloC+iu3LARn8uEznDASzjgjSkGj8D+IwfQ4JZzYHMl2dmmyikxnyRnuqrtOJupY01u9YbidUft99itgGOm9B2LIaqQQLHjRnu8PgQVm0QWkOQO0LrkHkUsblBsBi7TWgwzz4DzXgBfMPZQPRyZSzRQYrMYOqiJQYosk0AxjFuYvL2sxsgOqpjL4s+b48v8KR8+W+NJqxhsD9H4asOtYzTmzvdyIvFPXz34DG477DaKAj4WTKp2PvSJdUhSKAavlYFDfHTs9XjxJJr04iXsD0PNDJh0onNfmolnDhFMd1HUl7ooop2yQFwx5MpisC+ukspiyP5Lg3DZOjj69tgmbxqXi9hGmi1R57UIB5wdYUeBcPv+fkVjmV09O+kYexwp1fddd2x8IciALbsoknGgJY5STlfS7Yuncuqcocyui1tDlsUQbe3HuP7j4tttri4/IGnukeVKSlJ2dlK4km5tX8KmaGXS4Y4+otwcwJTbXMSeeOJM6rkq2dGrFYPd1xdU0aScco/Xj4f4MpNBF5PTExs5AP0Nn3aR6Z5wzmOwgs+h+P+BRuNRYs34jCISH9F05EoC8GdQDBBgTsudcKFRyveU6tkMb2+PpdOK8js6lGA6V5KNrxxYx60n70d52N+lGINX4hZD2O/sVJyKIeG32vylbp8JqLj7wStehy8eYICvnNqShBiKSf9gZqPrSQMmAVDmLevgSAOHYuhCkTI7fk/HwedOUVThsBATUzXtiK2j3mNWE10x+9dw6HdiE/wyxT57W1Ks3ufLMmjvsbWjtjRrPKckwZU0tKKI7580xZHOalkMu9ZewR+Pi8/DKPIldLbpFEMwA8VQPBCqJsOgCY7NLQR5MWJMiHRkJdkti+mnw6m/M/7HfpvXdmxunEC92pUUjhrlqMEsmhYoJtGVJO1xV1JAkrVpZUSxyyvGtPJ+hmIo9oWhPWFEGAs+J48slVjpixEiSKxRRG2rTqfCPmHp6u07GOYyn+LVq+axtz0KIePcnqDx4FrlPwR/LB8bOogx2KguD3HKbGMORo2Z7jalNgt3hYnXI7GKm8EEM9vj6PQTHqhJJ8C7j0K1My3U6gR8Hj97txxFsOpJ/B5/khVwZvkXOPEIZ+Ezi4qQe2A0kW/O/CaLRi1iUmA+OwAAIABJREFU29sus3hdKLcVVMsq+HzMj2IDj0TsFsPSCUuz+NLsSDeatFsMVRVV/HrZIUwaUgYiBFqbUn7ODftcjBHF093P1wX919oJxaASgs9uWFlJiX16KDFelUb4mGJI9wN9QbjgJXc5scdAjO/w2gePIkneBGWbG+TLMOmiI3q3YlBCk9k5BpUyO21bIMbnR9ptFoNtMlJ782h8JR9w06a9rCvaTXlUwUEXA+ZkpxbwlQ+D0541PpA4j8HG3qAxsl2nqhkudtMvO4thSVOz6zGDyhLOaSonK/jsIYDYfneRSxXWjphS24+nLp3L2EHZ124Xm8UQCjiDo05XmVBXZmvkk06ECcclBeFiriSvgFjBYS/eBIthb+XUlNlH6dwmdvxeP5MGTKKBhoyO77QrafbZKXdZiqEiVMGofqNSHtdVUhfRA4/tHrRLGZNr4gow0wwvC8s1FNk9LFakLvmYrL7SQatZYG9L9WFUbX4+sw+ZNcN8aa5ByO/l6/PGcNR+1Y7twSSLIbXwxZlYDGmwKq6mjDG4YnOf5ygdvVe7ksI210QwZjHEsdIbrQXtg7ZOfc/Gr9C85nKqIhFObN7FttNWQM1MAIrMh99XXGnMOITkmc82dvabxM9rbudn7UYNpZjF0JrsLwSIlMTnLGQ8k9WO6aP0m63HS8DhSipOM5EJMKbWz78xafP46jKHyZ4p9uBzKOEe+LzCoL1fZnjZcF6/Zj4TBiQ0cpdGb10/r9gUg3jxJ3ZQLkHAGJG9qfd1gXwEn63OSuXINZUK8abuNMQ2IdArTgXs76g9JRBvi5KyPXky1Az7j0i2/CxX0urD7obrkutPuZGJxQDwjfljGV/tdCsmKZOuupLS4OZj8GZRDC9XMYYuqRcRqQB+B9QB64EvKaWS7pSILAOuMd/epJS6X0SKgD8AozBqv/2vUuqKbM4ftjni4hZDfJvXZ6SrtsQsBlunrgKo9gCYGUT2EVOx+fD72mydSxqLAeBfxTNpZxMiErMCWjadxPjgRtaGtjuO3XHGiwwMGY07sfZNRpgdohVj8EqCYujIYjgvw1FWhnhFYqWYwwkLBF0ybwyoMRw0+vLMv89j3ROJlS7xeXyxct0Wrvny/tFEN6yEvdm5PzKlPGBzJX31uZx8Z7pRbC7xpeng7SPNRMWQbUDcOl6RPPDZu3UB/v6v4BHBG4VIB1/923P2pz0h2Lxk9jB++tc1VJWH4+VLOiIhxpANScokzfUozST4nMCQ8hCf7DQy8KyJhvZfnGr+SUwcR12lwgg+XwH8VSk1Bvir+d6BqTyuB/YH5gDXi4iVCnC7Umo8MB04WESOyubkgSTFEE7ISjKK6MXSVV3iAx4zOCYOxWD42b0RWyGvxJnPiSjr+2yNQgUY2Zo8OcgbLoMSozREpsFnBzGLwZxv4Qk4cp1LcjRqyBT74C8UcrqiDhpVyUGj3S2njjA6lbhiSFwO0m0kNfnYXzFl8mkwKvVyo13BbjH4KsemOTJzrBF5R6sKdpV0mVr2GINPuuantlsMifeotfFwdn1wJR6Bz9ZcR/Oaq9J+l8/rccwOBmNU//KVRzBpiLubyg3LYkgsopcJScokjWLojCvp8qPiZTgilsVg+7i3A8VcHHKfx9AVuqoYjgfuN1/fDyQnLMMCYIVSaodpTawAFiqldiulngNQSrUCbwDuKSYpsC9sEYoarqSiVmOy1uAts4wYA/HCX/bA6LgqqwMz/fR2xRA2zFdfxFbIK40rCYgtsSiIY5TklopnbzR+e1neySfDkTcnHZ+E36kY/BJwKMSSFKtj5Qv77ykKZTgBKw3t5sxzjwiXHj4NgAGhAUkxBtcAX2kVHPsTY82DPOCIMeRopB9zJeVZMaSLu3hsrqRM54Ck/C5btzK0v/vzIiIQLYqt75Etg8uzy5RKnODWJTJxJWURRDl+Wg3rbzEWdIoqQz57eD1VVdyYOLbMr4JwJQFVSqlNAEqpTSLitvhvDWCvObzR3BZDRPoBxwI/7awglivp7Mmn890nptOEF59vK0J8BmDQFhh96NwDWNe4C7nXlMH2kBcNng7vgm9ivLBdzGTNoGiafXEQNzPcrgwcFsMX7+3wu4FYLMVqAgFv0HGeMn+ALZl9U06wPwRFoc496HasPHWfVzh32hnUlA3k2FHH8sbavzvP24WFSDpLPhRDfNJVfhWDJ41Lwj5HJNFll/V5zPsyaXA5+49MtpjBsKx/d+4B7NjV6ro/55jzGHIyTyQDxdCZWB1A1CX43JFiiNrX9c6RxdBhyxaRZ4Bql11XZ3gOtysU+90i4gOWAz+zrwXtIse5wLkAoTqjc7ZfkJBSNPztVeoQrGlr2zZ/ghCvMrh9++dsamhIkESBwOv/+AeetUZ3un7vegDWbfqcBvP4sZu2UOUJ8OLzyf755uZmtm4zfITvvruacKOX6iJh825Fa2vy0pYvrXwxtoBI1NYZNCTKlgJv+27mEs9KiuyN8Nmez2Ka4vNtjTT7mzP+vq6ilOL67Y3c3a+c11a9Scifulk1N3cs11vbjJTdnZ99xsoXVlJOOS9sfIEPd65zHPf++/+mYXtugsyZyAWwN2qcTxBeeP6FnJx7V2QXAG3tbQ4ZMpUpU175+2sEg+4Dm40fx1cu+3zHrrTn7Uiu9/e8D0CkJfX3rFy5krBPCAMNDe93JHpGpJNLtZeysHwhoY9DNGxxPyZTWva28kqK8+wxC6S17W2hoaEh63touZLsFsNLL72Mx586W7A90h47R3tLboaEHSoGpdQXUu0TkS0iMti0FgYDW10O2wiO9TxqwZEbeDewRin1kw7kuNs8lvCIsOn/8YG1noH4OfTwI4yDn/4zAOPHj0NWx6eG1w0fR019veN7t5mSzJw5m/Iaoyb92s/W8sPHfsj4seOpH28ev3cFfP4P6hM+D0aHPmBACWzZwn6TJ3HE5MHcMWIHX/zVy+yxR9eUB9U0jnn19c4RhSmv23e7Eo3AyrgrqX9pBQO8Ama8dWRNLVuKSzL/vhzw52cn8ZemV9m77PCUpQXAuFYdyRV5bwu8voqBAyqor4+vBf3OxoFGJMtk8oSJ1E9MnlXbGTKRCwwl6PsfHyKSs+vbGmnligeu4PTJp1M/Pf6dmcrUIaaz95BD5lJc7J7J9eHzm430EWBQ5WDHdbd/B0BJSfq2Fd4Uhr9A//79k497ymjrhx06l6JAboPuKa/XU38GhB+c8IPOf7nt94fC4ZS/PxpVyF//j+LiIurr67O6hw+P+JSHX/4n/7vXuR703EPm4ncp6GjJZG+Lu5s+gT9ldLq0dNWuehxYZr5eBjzmcszTwJEi0t8MOh9pbkNEbgLKgUs7c/JPAvF6La651uJzrG4UDCZrXSuir2y+x37BfnjF63AbcNDX4NSHEj8e4/z6UYT9XmbXGfGJWXUV/OCLU5hlVqAUpej3/tdp/nhZp83MGKasVrpqyBd0TnDL0ezHbLi07WJmtvwyef2HTmBloSS6ivwJi+J0JRe+s4gIZcGynGYSBbwB/vmVf3LxtItz9p1ueNL41z1mTMYTleRaRJ0k3SzuTNNVC5I0riSPRygO+LKKMVjMHN6f4QONgLqjekwH7q+ozb7wuEzi7QxdbQG3APNFZA0w33yPiMwSkf8GUErtAG4EXjP/vqeU2iEitRjuqInAGyLypoick83JI7YO0LXKZkLedsDF/11q+gRLQnHFMiA8gN8t+h3z6+bbDqyG2pkpZZkxrD/v3bjQsYLU4llDqSo1y2uAo+Z9LrBcSZXFJQ7fqeQiwJYlbfhopLzrSg+oHzeQE6YN4YbjJjq2BwLOe7y73X1CYL4pC5R1KrslHR7xZL8YT5ZImhhD1BoEKX+nc/AtMgmi92a90FFHXRL0dfoaWuUv7FcwVbu4de6tgNOlHvCHqG1r41ufft6p81t0qXUrpRqBpLxApdQq4Bzb+3uBexOO2UjnqjwzrK2d+z7ZwqXxeWKEXOq5iMfnyPENBJMVQ9Dngb3gS5jdaK341VWsm+pTKjarMZGhFWEWzxya9XdbJTHGDOyP50NbJ5mrQmw9RNDn5SdLkkspJFqFqUpu55uyYBk79+7skXN3BUljCXjNa9keDXZZQSlbhl4qutNi8HkkaS5El+gofTTo7bxi8Fqpy7ZtKSblTRlolKt3Bp99PLlxE7t8IX7Yv/OJIL2yJEZpNMrMvc6gY8gljVR5fI6uOOBWWK0r1eMywCpV4YWUiuHFy47o1HdbMYaQL9TLh2CZEbAp790bzmX8gsk9IkdZoCw3aY/dTLrOOOa2U/60s/FvPuRmZ46hCzGLIU2T7E7F8M/rj8xtInBHFkPI71w3Nxs8yYohVTVXV3em+fnw7HNg8+Odk4FeqhjcCLmVTfZ4Y23TrxQelxiDfeGLfGC3GFozLG6XKZZiCHqDtNkbTy+3GFJhX5R9ePF+VCfWkOomKkIVjjWqC52HN27ivWAgvWKwZitH/UnrFts5btRxNHzUkP6ELmsdJJIDj2PGdHW9EYAnTnyC4h3rYd1xHT5fA0uCtLRFOnUeqzpq1HavUikG1/IeXh9ctwOPeCh68Bl2t+/ulBy9WjGMryrjFTM7K5jClbTBrKXeJgJBl8lXebcYzLo/pLYYOkWwLFYsr8RfQqMZrfIq1WOKobIkvxPrAraY0bPfqs/rudJx3pTz2LrbLQGvMBnb1sbYtrak5TDsxCwgFaBfuGv3MbY6WhrFkO94Sq4ZXjYcWoyV+zp6vv7rxMkZldx3I3HNEeN87tcqpdVqbi/2F++biiFomyofDLik4SWaWoF0s3Lz01CtoLBPqdjklZzwnbUMEOG/t77B1IFTufedVYAZkO4BxfDUpXMZVJrfEbw/TRG47mRY2TCGlQ3raTGyJl1nHLcYfK7rSj/3pefYYy5p2hHxZTN7V+ffIdZs+g6er6RqyFmgXOKgqfqmjjLjiv3FbNtjlJP3KUV7FvejMJ60HBBymQCSZIK5KYaKEfDxjrTrxnaFuCspxxaD2Uj3H2wsC9oeMR5GQzF0/wOZWJEyH3RUGVPjzn+i1Yz0bE57TDzGEKA87LJuSTj7elc5XXSoELCSH/L4fLnO5k9xvo4y44psteH8+6pisBavsZN0HdysiqW/hw9fAbcJJDnBciUZWUn5alPjqvvBWlMxBIqNerV9jD43Au0mTmj9HoPkM55Jc0ysLEfUR7+iLrqS8lzao8ew4kp5tMjdS3Zk6UoyKbbVhstqUSl6uWKwRiQ/27INBicHn4WElZ5cqqtSXAkTFuVDPCCefeE1LYbOTHzJBGu2ccBauzo/Vad7nJPHnMyCugU9LUav4nNK+FylL24YW+JSBVxdSdmQSVZSryRDV1JXcFUMnbQYin1xxeD3+slmtNirFYNjIo2LNSCJI5ceKLoWcyWZFoM/T+kYVoPyK2Uouz6qGG446IaeFqFPYs9KcnMlZcOAkFE4b2y/3JQlLxi8+VcMriW2UyiGjgoC2l1JPn8xtGU+6a1XKwYHbuWwC8D1EA8+g8KTxzQ94zxBpQyLgcwChRoN2LOS/PTromKYVDmJ+xbeF5uA1WfoBsWQTfXXjlyrdleSP8sSLr064d0R3HJxE7VXTU3a1v3E01UhfxN7rAYVtCwGjSYLrPpIKuqnrIuKAWBm1cyslwQteDxeY12WAlEMFjUlNa7bR5aPjL32Z/m9fcJiUODuSioAi0FsMQbIn9vVUjh+paBoAB1OT9VobPhsFkPiimm55KlL5/LWxt5XTiSGL5hfV1KW7u7fHv3blIrhtAmnsbN1J7/656/wZZnR1ycUA+BqMRSAXoitZmXdlnwpK2umZAAFocyXPNT0fUQ6rtDgscUY8sn46rJuSW3OG95Ani2G7PqHdO46ESESNQLO+65icLEYCqG0rzWXwmfaCvmSqDVqLG4TjPbMPAZN4fLct+p5f0v6bIRiXxEq6kO1p14QRkPeFUOuVyVsV0a/sE8ohnZ8rIxMoqq4CoCSaNQ1+GzvHm/fsq2bpEuQQZwxhnxphlazwmIf8+pqckBdZTF1le4L9FiUBErYtfZbnV6DeZ/BF8zvBLccKx3LYvBnOYG3VyqG99Qwvtx2Nf+aNY9pm9cwe92j7nMUbPdv7vzbu09AhwiWKym/FkObOTIIpCuIo9GkQARUe/+eFqPw8frz60rKtcVgehL8Wa4f0iUpRKRCRFaIyBrzv2vLEpFl5jFrRGSZy/7HReSdbM8f8oVYVDTU6Gzdgs+2btg7ZXG2X58TEi2GfMUY9ipjZOC6kp1G0wFdXZxnn8Gb5+Bzri0Gs1/wdbPFcAXwV6XULSJyhfn+cvsBIlIBXA/Mwkggel1EHldKfWruPwnowlJcZoPuIPjcU3V2PFgT3EyLIV+uJHNkEPD1nnLQuaatrY2NGzfS0tKS9WfLy8t577338iBV5+msTKFQiNraWvz+zB2LVjyuLE3JbQ3m7Ofe40qyLIbujjEcD9Sbr+8HGkhQDMACYIW5xCcisgJYCCwXkRLgm8C5wO87JYHV07paDHF6rACbOF1J+QqIzykbBcBCb0Vevr83sHHjRkpLS6mrq8vaMmtqaqK0tLACr52RSSlFY2MjGzduZMSIER1/wKS13YhRlYZ0lCotM8/ooEpz18i1YrCKHw5wKTKajq4qhiql1CYApdQmERnkckwNzqT6jeY2MNaC/iHQuaLhEDfrXILP9k64p+Y0xNdjyG+MYbQnxNvrPoQJ0/J0hsKnpaWlU0qhLyEiDBgwgG3bsku26FdsKIQL6kflQ6y+w6yz8vr1nZnglo7zppxHXXkdZdvX8sjWv2f8uQ4Vg4g8A1S77Lo6w3O4PaVKRKYBo5VS3xCRugzkOBfDsiBQPRqAhoYG6j76hGHi44WVryT5aV566aXY64aGhgzFzZ7m5uaU37/1E0MnSsQYkbW1teZFlupNrzMe+HhnO2saGtLK1JPkU67y8nKamzvnlYxEIjQ1FVaBqa7I1NLSkvV1vm9hMbSso6FhXdrj9sW21Vmylen9pk1J27r6m0ooYf3WDVl9pkPFoJT6Qqp9IrJFRAab1sJgwG1Zq43E3U0AtRgupwOBmSKy3pRjkIg0KKXqcUEpdTdwN0Bw8BgFUF9fDzPGwpbF1I85PH7wU38GYO4hh8AfjE319a5fmxMaGhpSfv+rf3sN1sRXHwsEgvmRpe0AeKadmsOvpCZUnlamniSfcr333nuddgf1FVeSRSgUYvr06TmWyGBfbFudJVuZSj5+n8T66Ln4TStXrYbVKzI+vqt2y+OAlWW0DHjM5ZingSNFpL+ZtXQk8LRS6pdKqSFKqTrgEODfqZRCWsqGwJj57vsKwKNgTXDz5Dn4jD8ER92iZz0XAI888ggiwr/+9S/H9h//+MeEQiF27oyXhGhoaKC8vJzp06czYcIEvvvd78a2L17cM5l0mp4jX7HQbLudriqGW4D5IrIGmG++R0Rmich/A5hB5xuB18y/71mB6HxTCK7m+Mxn438BiKTJM8uXL+eQQw7hoYceSto+e/ZsHnnkEcf2uXPn8o9//INVq1bxwAMP8Prrr3enuJoCwpOntOFsV9PrkmJQSjUqpeYppcaY/3eY21cppc6xHXevUmq0+fcbl+9Zr5SanOl5x1aV8vKVR3R4XCF0wknB50IQSpM3mpubeemll/j1r3/tUAxr166lubmZm266ieXLl7t+tri4mJkzZ7J27druEldTaKg8zZHYF6qrBn0eBpe7rL+QQCHUSrKyDKyV2/rcOrgFynf/dzXvfpL5wiSRSASvN70ZP3FIGdcfOyntMY8++igLFy5k7NixVFRU8MYbbzBjxgyWL1/Oqaeeyty5c3n//ffZunUrgwY5k/gaGxt55ZVXuPbaa7POKtL0Ddoi+VkWtbtdSQVNAegFm8VgdDqFIJMmfyxfvpwlS5YAsGTJkph18NBDD7FkyRI8Hg8nnXQSf/jDH2KfefHFF5k+fTpHHnkkV1xxBZMmpVc+mr5LJgPeziBZdvW90mLIlIIYnVsxBsnvPAaNk45G9onkIiupsbGRZ599lnfeeccoeRyJICJ8+ctfZs2aNcyfbyRJtLa2MnLkSC666CLAiDE88cQTXTq3pm/g78Bq7SzZ1mDSFkOesdxZ1roM+/Lkq77OH//4R77yla+wYcMG1q9fz0cffcSIESO49NJLueGGG1i/fj3r16/nk08+4eOPP2bDhuxyyzV9H/sEt+c2bORPG5PnNXSGUl8R01r2Zi5HTs6qSYnEYgyWYuhJaTT5ZPny5Zx44omObSeffDLr169P2n7iiScmZS0l8vzzz1NbWxv7e/nll3Mus6awsHs5KqNRxrS15eR7J4w/if855ZmODzTp266kAuiEY+mqWjH0edxmqF5yySVccsklSdt/9KMfxV67TWCqr69n69atBTfpTpNffB6jS55dPRvWfZi7L/aHoN+wzOXI3ZkLj0KIMcTXY7DmMfS8TBqNpjApD5bzi3m/YGbVTHj54R6To08rhkIoMR9bj0FbDBqNJgPm1s7taRH6tmIQEVZ8cQVtkdz46TpDfB6Dma7aY5JoNBpNZvRtxQBUF7sVhu1GGUwTwVooQ2claTSaQqdPZyUVRh+ckJXUk6JoNBpNBvRxxdDz3bAnFmMwJ670vEgajUaTlj6tGAqBEp8xxb1MjBWytF7o24gIp59+eux9e3s7AwcOZNGiRQBs2bKFRYsWMXXqVCZOnMjRRx8NwPr16wmHw0ybNi32d++998ZeBwIB9ttvP6ZNm8YVV1zRI79N082c/Qwc/4seOXWfjjEUAoNDFTyycRMD6mYDhWHFaPJHcXEx77zzDnv27CEcDrNixQpqampi+6+77jrmz5/P17/+dQDeeuut2L5Ro0bx5ptvxt43NTXFjqurq+O5556jsrKym36JpscZOtv46wG0xZBvxMPotjbEXMFNq4W+z1FHHcWf/2ysImhVVbXYtGkTtbW1sfdTpkzpdvk0mo7oksUgIhXA74A6YD3wJaXUpy7HLQOuMd/epJS639weAO7AWPozClytlOq5WR15wVQFoqurditPXgGb38748HCkHbwdPA7V+xmr5HXAkiVL+N73vseiRYt46623OOuss3jxxRcBuOiiizjllFO44447+MIXvsCZZ57JkCFDAGPNhmnTpgFw8MEHc8stHZ9Lo8kHXXUlXQH8VSl1i4hcYb6/3H6AqTyuB2YBCnhdRB43FcjVwFal1FgxakdUdFGewsPUBMpa4lNrhj7PlClTWL9+PcuXL4/FECwWLFjAf/7zH5566imefPJJpk+fzjvvvAO4u5I0mp6gq4rheIzRPsD9QAMJigFYAKywVncTkRXAQmA5cBYwHkApFQW2d1GeAsRSDDqc061kMLK3sycHZbftHHfccXz729+moaGBxsZGx76KigqWLl3K0qVLWbRoES+88AIzZ87M2bk1mq7S1RhDlVJqE4D5f5DLMTXAR7b3G4EaEelnvr9RRN4QkT+ISFUX5Sk8EiwGHXzeNzjrrLO47rrr2G+//Rzbn332WXbv3g0YFsHatWsZNizz4mYaTXfQ4TBWRJ4B3KYPX53hOdx6QmWeuxZ4SSn1TRH5JnA7cLrL8YjIucC5AFVVVa6VLBPJ5Jhc0NzcnPJc5Z+tZjqweauxVOOuNMd2l0w9ST7lKi8v77T7JRKJ5Mx109TURHl5OWeddRZNTU3s3r2b9vZ2mpqa+Nvf/saFF16Iz+cjGo1y+umnM378eDZs2EA0GnXIYJdJKUVzczPBYDAjGVpaWvJ2nffFttVZClGmjFBKdfoPeB8YbL4eDLzvcsypwF2293eZ2wTYBXjM7UOB1Zmcd+bMmSodwy9/Qg2//Im0x+SS5557LvXO9S8pdX2Z2vrwd9Twy59QR//0hZ6XqQfJp1zvvvtupz/7+eef51CS3NAVmbpyLTpiX2xbnaXQZAJWqQz62K66kh4HlpmvlwGPuRzzNHCkiPQXkf7AkcDTppD/SzxGMQ94t4vyFCDOGIP2JGk0mkKnq4rhFmC+iKwB5pvvEZFZIvLfAMoIOt8IvGb+fc/cBkag+gYReQvDhfStLspTeJixhViMQc9k0Gg0BU6XUmWUUo0YI/3E7auAc2zv7wXudTluA3BoV2QoeGLBZz2PQaPR9A70zOe8Y2iCUMColTSltrwnhdFoNJoO0cn1+cY0EfqVhHn84oMZX13WwwJpNBpNerRiyDvxkhhTavulP1Sj0WgKAO1KyjdWTMHj7VExNN3Dli1bWLp0KSNHjmTmzJkceOCBPPLIIwCsXLmSOXPmMH78eMaPH8/dd9/t+Ozdd98d2zdnzhxefvnl2L729nauuuoqxowZEyvFffPNN3frb9PsO/RJi6GyJMD25taeFsPEWURP03dRSnHCCSewbNkyHnzwQQA2bNjA448/zubNm1m6dCmPPvooM2bMYPv27SxYsICamhqOOeYYnnjiCe666y5WrlxJZWUlb7zxBscffzyvvfYa1dXVXHPNNWzevJm3336bUChEU1MTP/zhD3v4F2v6Kn3SYnjq0kP5v0vm9rQYBlYakqdP6mCNjWeffZZAIMD5558f2zZ8+HC+9rWvceedd3LGGWcwY8YMACorK7nttttiFVRvvfVWfvCDH8TWW5gxYwannnoqd955J7t37+aee+7h5z//OaFQCIDS0lJuuOGG7v2Bmn2GPtlbVZYEqSzJrHRA/rEUQ5/UwQXLra/eyr92/Cvj4yORCF5veqtufMV4Lp+TWCMyzurVq2Mdv9u+ZcuWObbNmjWL1atXx/YnFtKbMWMGv//97/nggw8YNmxYTov8aTTp0L1VvjEntmlX0r7HRRddxNSpU5k9ezZKKdcCiumKKqb6zG9+8xumTZvG0KFD+eijj1w+qdF0jT5pMRQUMVeSVgzdSbqRvRtNOSi7PWnSJB5+OL7O1J1apJzmAAAPeklEQVR33sn27duZNWsWCxYsYNWqVRx33HGx/a+//joTJ04EYOLEibz++uscccQRsf1vvvkmEydOZPTo0Xz44YcxGc8880zOPPNMJk+eTCQS6ZLMGo0b2mLIOzrGsK9wxBFH0NLSwi9/+cvYNqvE9kUXXcR9990XW4insbGRyy+/nMsuuwyAyy67jMsvvzy2dsObb77Jgw8+yIUXXkhRURFnn302F198MS0tLYDh+mptLZQEC01fQ/dW+UZ0VtK+gojw6KOP8o1vfIPbbruNgQMHUlxczK233srgwYN54IEH+OpXv0pTUxNKKS699FKOPfZYwFjY5+OPP+aggw5CRCgtLeWee+5h8ODBANx8881ce+21TJ48mdLSUsLhMMuWLYstC6rR5BKtGPKOdiXtSwwePJiHHnrIdd+hhx7Ka6+9lvKzF1xwARdccEHsvX1tBr/fzy233KLXgdZ0C9qVlG+KKgw3UungnpZEo9FoMkJbDPmmtBq+sxbCuhyGRqPpHWiLoTvQSqHbMNZ/2rfR10DTVbqkGESkQkRWiMga83//FMctM49ZIyLLbNtPFZG3ReQtEXlKRCq7Io9m3yYUCtHY2LhPd4xKKRobG2MzpDWaztBVV9IVwF+VUreIyBXme0cCuYhUANcDswAFvC4ijwNNwE+BiUqp7SJyG3AxcEMXZdLso9TW1rJx40a2bduW9WdbWloKrjPtrEyhUIja2to8SKTZV+iqYjie+JrN9wMNJCgGYAGwwlrOU0RWAAuBP2Kk7BSLSCNQBnzQRXk0+zB+v58RI0Z06rMNDQ1Mnz49xxJ1jUKUSbNv0FXFUKWU2gSglNokIoNcjqkB7PP2NwI1Sqk2EbkAeBvYBawBLuqiPBqNRqPpItKRP1ZEngGqXXZdDdyvlOpnO/ZTpZQjziAi3wGCSqmbzPfXAruBnwFPAecC/wF+Dmy2jnOR41zzWKqqqmamyhXvCZqbmykpKelpMRwUokyg5cqGQpQJtFzZUGgyHX744a8rpWZ1eKBSqtN/wPvAYPP1YOB9l2NOBe6yvb/L3DYbIz5hbT8U+L9Mzjtz5kxVSDz33HM9LUIShSiTUlqubChEmZTScmVDockErFIZ9LEdWgzpEJEfAI0qHnyuUEpdlnBMBfA6YNUjfgOYCYTM7VOUUttE5EagSCn1rQzOuw3Y0GnBc08lsL2nhUigEGUCLVc2FKJMoOXKhkKTabhSamBHB3VVMQwAfg8MAz4EFiuldojILOB8pdQ55nFnAVeZH7tZKfUbc/v5wNeBNoyO/gylVGOnBeohRGSVysQ860YKUSbQcmVDIcoEWq5sKESZMqFLwWezE5/nsn0VcI7t/b3AvS7H/Qr4VVdk0Gg0Gk1u0TOfNRqNRuNAK4bccHdPC+BCIcoEWq5sKESZQMuVDYUoU4d0Kcag0Wg0mr6Hthg0Go1G40ArhgyRdKu2axzoa5U5+lpljr5W3YdWDJnTD0BECmYNCxFZKiJTzdeF9NDEKr8VmFyFiG5XmVOw7UpE+lRf2qd+TD4QkXIR+QtG+Q6UUu09LBIi8gUReRH4CTAdQBVAsEhEjhSRvwF3iMhpUDBynWBOoCwYdLvKnAJuV8eJyDd7Wo58UDCjlAKmBfgUOFhEFiul/iAiXqVUpDuFMEdIIYwqtoOAmzCq2xaZ+7tdpgT5BgLfA27BKKn+dREZppT6voh4lFLRbpZHMAY+Z2KUgx8uIn9RSr3YnXKkQberzOQrqHZlyuQDvgVcAAwTkWeVUm/29LXKKZnUzdhX/wAvUAV8A1iEUeTP2ic9JNPxttdfBl4ugOskwGScNbEmAo1AZQ9fr3qgFPgq0NDT18qUSberzGQq5HZ1AoZCvRT4e09fq1z/aVeSDRG5RETuEZGzRESUof0/B45RSj0BvCUi14nIZKWU6g4/p02mrwIopR4zt3uBdcBqERmabzlc5FomIvNNmRTQDBxk1sZCKfUu8AeMqrndKZd1vayZ988rpZqUUvdgrP1xtnlct7V93a6ykquQ29UtIvIlc9OflVItSqmfAINEZKl5nL875cobPa2ZCuUPOAN4BWMRoecxajuNwjSvzWPOAtoxKxQC/h6QaaRt/37Aa0BpN16n/hiLLG0C3gK8tn3/D/ifhGP/DozooXt4JTDKtv8oYDXQX7cr3a4ylEswLLuXgC8C75nXb5DtmBOBj7vrWnXHn7YY4swDblVKPYXhPwwBi4E9wFFmoPAS4FnilV3zHTBMlCmAYeYDoJR625RvSZ7liKGU+hT4CzABozrudbbdFwMLRWS2+X4X8E+gtZvEc7uHp1k7lVJPYjzY54pIqYgs7iGZdLtKoFDblTJ6/sOBa5RSf8RQElMxVqa0jnkE+LeIfBuMIH6+5co3+7xisLkU/oHh70UZRQD/BowADgFWAK8qpaYppY4E6kVkhNloulOmV4AhInKweZxgPEyhbnI/WOf4f0qpz4BfACeJyHBTxs+B7wLXisgy4BoMH3FznuVKdb1exna9TC4Hvo+xYqDbAlT5lkm3q2S5CqJdJf5W2/VaBcw1ZXkK+DcwSUTG2Q6/ALhNRDZjrFrZq9nnFIOITBKRWD60imc1vAR4RORQ8/1q4GOMwOV1SqlrbF8zTCm1rgdkegfD1B5iHqcwXBK78tGZuMilzP8t5v/XgCeBm23H3IGR7jgTGA58USm1M8dyHSwio2znzOh6ichojE7nUWCGUipnfuosZOrOdtWp69QN7SpRroJoV0DY/sZ2vT4ASkVkP/P980A5xj1ERKYB9wAPY7Sr+3MsV7ezzygGEZkiIisx0vEG2LZb12ANxkN7ipl29hHGgzJcKdUqIl7rWKXUrh6SaSPGKLfO9jXfVkZZ85yRRi5xCdreAYw2lUiViIxWSj0LfEMptUwp9UkO5Zphul6exXgwre2ZXq+dwMVKqZNyJVcnZOqOdtXV6wT5aVep5OrpdnWAiDwM3CnGnAmvud1K538ViADzRcSnjAB4DWCts9AIXKiUWpxLuXqSfUYxYJiff1RKnaiU+hhiOdrWqKAJeBHD33q7mV3QD+Omo5SKqNznTHdGpv6WTKZc+fCzppJLKaWiIhIWkRLz/B8CjwBvY4ykysztOcvnFhG/iNyFUanyZ8DTGGmoWV0vpdQ2pdSaApApL+0qV9fJlCtn7SoDuXqkXZnnr8ewIv+EsVTxl4H+YsyRaDfP+QFGMH40xpwYgL2YMSGl1EdmXKbP0OcVg4h4TLO1WRmpZYjIfBHph5FxgIjcBDyIMaK8DuMhedF8n3OzsBBlykKuG4HfAiPN96cCFwK3A/sppd7Ig2hB4AVgrjLSO/8ETDBHbxFTju/SvddLy5Rbua6n+9sVwBTgNaXUb4EHAD9G+4+actwkIr/GCIj/DJgjIq8DOzAUXN9EFUBqVK7/gAOAsbb3pRjm8yIM//LTGClwV2KYzw8Co23He8hxql4hypQjuQ4gD2mDdrlImMQEnA38ytqH8XA/iDM1Na/3UMuUd7ny3q7M99MwOvnrgS1AA8Zqk6cAB7m09xKgX67lKrS/Hhcgxze9H/BnDFP5GqDYtu8q4A3gOPP9ocBjwIG2Yzz7gkw5ksvbnXKZHYjHfD3afIj7W/t64h5qmfIiV3e1qxLbvjkYyuBk8/3ZGMHkqfm+XoX619dcScUYI9yvma8Pte17AmPEW2G+XwVsxqhZg+Sv7kohypQLufJVE8ZVLmUQNYOU681jDrP22eTqtnuoZcqLXN3VruZaO5RSrwIDic8jeRZDkXxqk6vbazL1JL1eMYjIV0TkMBEpU0ag9G7g9xid2P4iUgOglHoL+A5wkYhUYgSZ9iMeBMzZjS9EmXq5XFa6qZjntlJoLUUluZZLy7RPyRXEmF9yofnReRiDIit9dp9SCtBLFYMYDBaR54BlGLNbfykilcqoX7IbeAYjsHaE9Tml1K+B5cANwMnAOcrIfuiTMvU1uZRSysxiacZwTRxgbdcydZ9MfUiueeb59wKPAyUi8gJwKkZa89ZcydXryLevKtd/mD5IYCzwgPnah1FU608Jx34DIxe/HFuAjRzXoilEmfqoXEUFeA/3KZn6oFz9gLC5LYytZtS+/NdrLAYR8YnIfwH/JSKHAeMwJp2gjHzjS4ADzX0W92BkEawAPrBMR6VUW1+VqY/Lta4A7+E+IVMfl2u9iNQopfYopf6TK7l6M71CMZg39HUM8+8D4EagDThcROZAzBz9Hobrw+IYDL/hPzFyoXM5W7LgZNJyaZnyIVMfl+tNU66PcylXr6enTZZM/jAyCE63vf8FRtGqM4DXzW0ejGn9vwfqzG3HA4fuKzJpubRM+9L9K2S5evtfjwuQ4c0vwpg9afkQTwO+b75+E/ia+XoWsHxflUnLpWXSchWGXL39r1e4kpRSu5VSe1U8x3k+sM18fSbG9PonMLJo3oDkErr7gkxaLi2Tlqsw5Ort+Do+pHAQo+qhwlgv93FzcxPGTN3JwDpl+gqVOUzYF2XScmmZtFyFIVdvpVdYDDaiGEWutgNTzJHAtUBUKbVS9UwAqRBl0nJpmbRchSFX76SnfVnZ/mFMiokCK4Gze1qeQpVJy6Vl0nL1vEy99U/MC9prEJFa4HTgR8qYsdjjFKJMoOXKBi1T5mi5+j69TjFoNBqNJr/0thiDRqPRaPKMVgwajUajcaAVg0aj0WgcaMWg0Wg0GgdaMWg0Go3GgVYMGk0HiEhERN4UkdUi8k8R+aYYS1Sm+0ydiCztLhk1mlyiFYNG0zF7lFLTlFKTMGrxHA1c38Fn6gCtGDS9Ej2PQaPpABFpVkqV2N6PBF4DKoHhwP9gLDAPxpKQfxORV4AJwDrgfuBnwC1APUY10DuVUnd124/QaLJAKwaNpgMSFYO57VNgPEahtqhSqkVExmCUdp4lIvXAt5VSi8zjzwUGKaVuEmPx+ZeAxUqpdd36YzSaDOhV1VU1mgLCKt3sB+4QkWkYy0mOTXH8kRjF3b5ovi8HxmBYFBpNQaEVg0aTJaYrKQJsxYg1bAGmYsTsWlJ9DGPRmKe7RUiNpgvo4LNGkwUiMhD4FXCHMvyw5cAmpVQUo4Cb1zy0CSi1ffRp4AIR8ZvfM1ZEitFoChBtMWg0HRMWkTcx3EbtGMHmH5n7fgE8LCKLgeeAXeb2t4B2EfkncB/wU4xMpTfMFcS2ASd01w/QaLJBB581Go1G40C7kjQajUbjQCsGjUaj0TjQikGj0Wg0DrRi0Gg0Go0DrRg0Go1G40ArBo1Go9E40IpBo9FoNA60YtBoNBqNg/8PpKqk6yWl12sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stock_change.plot(grid = True).axhline(y = 0, color = \"black\", lw = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>SPY</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>101.783763</td>\n",
       "      <td>52.181598</td>\n",
       "      <td>741.84</td>\n",
       "      <td>201.0192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>99.233131</td>\n",
       "      <td>52.419653</td>\n",
       "      <td>742.58</td>\n",
       "      <td>201.3600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>97.291172</td>\n",
       "      <td>51.467434</td>\n",
       "      <td>743.62</td>\n",
       "      <td>198.8200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>93.185040</td>\n",
       "      <td>49.677262</td>\n",
       "      <td>726.39</td>\n",
       "      <td>194.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>93.677776</td>\n",
       "      <td>49.829617</td>\n",
       "      <td>714.47</td>\n",
       "      <td>191.9230</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  AAPL       MSFT    GOOG       SPY\n",
       "Date                                               \n",
       "2016-01-04  101.783763  52.181598  741.84  201.0192\n",
       "2016-01-05   99.233131  52.419653  742.58  201.3600\n",
       "2016-01-06   97.291172  51.467434  743.62  198.8200\n",
       "2016-01-07   93.185040  49.677262  726.39  194.0500\n",
       "2016-01-08   93.677776  49.829617  714.47  191.9230"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import pandas_datareader.data as web    # Going to get SPY from Yahoo! (I know I said you shouldn't but I didn't have a choice)\n",
    "#spyder = web.DataReader(\"SPY\", \"yahoo\", start, end)    # Didn't work\n",
    "#spyder = web.DataReader(\"SPY\", \"google\", start, end)    # Didn't work either\n",
    "# If all else fails, read from a file, obtained from here: http://www.nasdaq.com/symbol/spy/historical\n",
    "spyderdat = pd.read_csv(\"HistoricalQuotes.csv\")    # Obviously specific to my system; set to\n",
    "                                                                          # location on your machine\n",
    "spyderdat = pd.DataFrame(spyderdat.loc[:, [\"open\", \"high\", \"low\", \"close\", \"close\"]].iloc[1:].values,\n",
    "                         index=pd.DatetimeIndex(spyderdat.iloc[1:, 0]),\n",
    "                         columns=[\"Open\", \"High\", \"Low\", \"Close\", \"Adj Close\"]).sort_index()\n",
    " \n",
    "spyder = spyderdat.loc[start:end]\n",
    " \n",
    "stocks = stocks.join(spyder.loc[:, \"Adj Close\"]).rename(columns={\"Adj Close\": \"SPY\"})\n",
    "stocks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x2108b4eea20>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stock_return = stocks.apply(lambda x: x / x[0])\n",
    "stock_return.plot(grid = True).axhline(y = 1, color = \"black\", lw = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x2108b597c50>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stock_change = stocks.apply(lambda x: np.log(x) - np.log(x.shift(1)))\n",
    "stock_change.plot(grid=True).axhline(y = 0, color = \"black\", lw = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>SPY</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-03-21</th>\n",
       "      <td>-577.463148</td>\n",
       "      <td>-176.499833</td>\n",
       "      <td>-157.285338</td>\n",
       "      <td>-48.409635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-22</th>\n",
       "      <td>-359.355133</td>\n",
       "      <td>-743.873619</td>\n",
       "      <td>-984.592233</td>\n",
       "      <td>-637.937081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-23</th>\n",
       "      <td>-589.663945</td>\n",
       "      <td>-743.366326</td>\n",
       "      <td>-669.637836</td>\n",
       "      <td>-542.932908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-26</th>\n",
       "      <td>1168.762361</td>\n",
       "      <td>1839.012005</td>\n",
       "      <td>768.649993</td>\n",
       "      <td>680.185034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-27</th>\n",
       "      <td>-654.582257</td>\n",
       "      <td>-1185.615651</td>\n",
       "      <td>-1178.241231</td>\n",
       "      <td>-432.385872</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   AAPL         MSFT         GOOG         SPY\n",
       "Date                                                         \n",
       "2018-03-21  -577.463148  -176.499833  -157.285338  -48.409635\n",
       "2018-03-22  -359.355133  -743.873619  -984.592233 -637.937081\n",
       "2018-03-23  -589.663945  -743.366326  -669.637836 -542.932908\n",
       "2018-03-26  1168.762361  1839.012005   768.649993  680.185034\n",
       "2018-03-27  -654.582257 -1185.615651 -1178.241231 -432.385872"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_change_apr = stock_change * 252 * 100    # There are 252 trading days in a year; the 100 converts to percentages\n",
    "stock_change_apr.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>2.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-01</th>\n",
       "      <td>2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01</th>\n",
       "      <td>2.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-01</th>\n",
       "      <td>2.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01</th>\n",
       "      <td>2.35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Value\n",
       "Date             \n",
       "2019-01-01   2.37\n",
       "2019-02-01   2.39\n",
       "2019-03-01   2.40\n",
       "2019-04-01   2.38\n",
       "2019-05-01   2.35"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tbill = quandl.get(\"FRED/TB3MS\", start_date=start, end_date=end)\n",
    "tbill.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2108b621748>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tbill.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.35"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rrf = tbill.iloc[-1, 0]    # Get the most recent Treasury Bill rate\n",
    "rrf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL    0.593097\n",
       "MSFT    0.720059\n",
       "GOOG    0.674322\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smcorr = stock_change_apr.drop(\"SPY\", 1).corrwith(stock_change_apr.SPY)# Since RRF is constant it doesn't change the correlation so we can ignore it in our calculation\n",
    "smcorr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL    339.921782\n",
       "MSFT    329.308164\n",
       "GOOG    312.319468\n",
       "dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sy = stock_change_apr.drop(\"SPY\", 1).std()\n",
    "sx = stock_change_apr.SPY.std()\n",
    "sy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "188.08029321745738"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL    19.319035\n",
       "MSFT    21.912806\n",
       "GOOG    11.316893\n",
       "dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ybar = stock_change_apr.drop(\"SPY\", 1).mean() - rrf\n",
    "xbar = stock_change_apr.SPY.mean() - rrf\n",
    "ybar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.331380746251185"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xbar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL    1.071918\n",
       "MSFT    1.260744\n",
       "GOOG    1.119756\n",
       "dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beta = smcorr * sy / sx\n",
    "alpha = ybar - beta * xbar\n",
    "beta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL     9.316557\n",
       "MSFT    10.148320\n",
       "GOOG     0.868025\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alpha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL    0.049920\n",
       "MSFT    0.059406\n",
       "GOOG    0.028711\n",
       "dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sharpe = (ybar - rrf)/sy\n",
    "sharpe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03711915069262122"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(xbar - rrf)/sx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "apple[\"20d\"] = np.round(apple[\"Adj. Close\"].rolling(window = 20, center = False).mean(), 2)\n",
    "pandas_candlestick_ohlc(apple.loc['2016-01-04':'2016-12-31',:], otherseries = \"20d\", adj=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "start = datetime.datetime(2010,1,1)\n",
    "apple = quandl.get(\"WIKI/AAPL\", start_date=start, end_date=end)\n",
    "apple[\"20d\"] = np.round(apple[\"Adj. Close\"].rolling(window = 20, center = False).mean(), 2)\n",
    " \n",
    "pandas_candlestick_ohlc(apple.loc['2016-01-04':'2016-12-31',:], otherseries = \"20d\", adj=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "apple[\"50d\"] = np.round(apple[\"Adj. Close\"].rolling(window = 50, center = False).mean(), 2)\n",
    "apple[\"200d\"] = np.round(apple[\"Adj. Close\"].rolling(window = 200, center = False).mean(), 2)\n",
    " \n",
    "pandas_candlestick_ohlc(apple.loc['2016-01-04':'2016-12-31',:], otherseries = [\"20d\", \"50d\", \"200d\"], adj=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Ex-Dividend</th>\n",
       "      <th>Split Ratio</th>\n",
       "      <th>Adj. Open</th>\n",
       "      <th>Adj. High</th>\n",
       "      <th>Adj. Low</th>\n",
       "      <th>Adj. Close</th>\n",
       "      <th>Adj. Volume</th>\n",
       "      <th>20d</th>\n",
       "      <th>50d</th>\n",
       "      <th>200d</th>\n",
       "      <th>20d-50d</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-03-21</th>\n",
       "      <td>175.04</td>\n",
       "      <td>175.09</td>\n",
       "      <td>171.26</td>\n",
       "      <td>171.270</td>\n",
       "      <td>35247358.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>175.04</td>\n",
       "      <td>175.09</td>\n",
       "      <td>171.26</td>\n",
       "      <td>171.270</td>\n",
       "      <td>35247358.0</td>\n",
       "      <td>176.94</td>\n",
       "      <td>172.57</td>\n",
       "      <td>162.68</td>\n",
       "      <td>4.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-22</th>\n",
       "      <td>170.00</td>\n",
       "      <td>172.68</td>\n",
       "      <td>168.60</td>\n",
       "      <td>168.845</td>\n",
       "      <td>41051076.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>170.00</td>\n",
       "      <td>172.68</td>\n",
       "      <td>168.60</td>\n",
       "      <td>168.845</td>\n",
       "      <td>41051076.0</td>\n",
       "      <td>176.76</td>\n",
       "      <td>172.46</td>\n",
       "      <td>162.75</td>\n",
       "      <td>4.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-23</th>\n",
       "      <td>168.39</td>\n",
       "      <td>169.92</td>\n",
       "      <td>164.94</td>\n",
       "      <td>164.940</td>\n",
       "      <td>40248954.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>168.39</td>\n",
       "      <td>169.92</td>\n",
       "      <td>164.94</td>\n",
       "      <td>164.940</td>\n",
       "      <td>40248954.0</td>\n",
       "      <td>176.23</td>\n",
       "      <td>172.27</td>\n",
       "      <td>162.81</td>\n",
       "      <td>3.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-26</th>\n",
       "      <td>168.07</td>\n",
       "      <td>173.10</td>\n",
       "      <td>166.44</td>\n",
       "      <td>172.770</td>\n",
       "      <td>36272617.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>168.07</td>\n",
       "      <td>173.10</td>\n",
       "      <td>166.44</td>\n",
       "      <td>172.770</td>\n",
       "      <td>36272617.0</td>\n",
       "      <td>175.92</td>\n",
       "      <td>172.22</td>\n",
       "      <td>162.91</td>\n",
       "      <td>3.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-27</th>\n",
       "      <td>173.68</td>\n",
       "      <td>175.15</td>\n",
       "      <td>166.92</td>\n",
       "      <td>168.340</td>\n",
       "      <td>38962839.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>173.68</td>\n",
       "      <td>175.15</td>\n",
       "      <td>166.92</td>\n",
       "      <td>168.340</td>\n",
       "      <td>38962839.0</td>\n",
       "      <td>175.41</td>\n",
       "      <td>172.05</td>\n",
       "      <td>162.98</td>\n",
       "      <td>3.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low    Close      Volume  Ex-Dividend  \\\n",
       "Date                                                                   \n",
       "2018-03-21  175.04  175.09  171.26  171.270  35247358.0          0.0   \n",
       "2018-03-22  170.00  172.68  168.60  168.845  41051076.0          0.0   \n",
       "2018-03-23  168.39  169.92  164.94  164.940  40248954.0          0.0   \n",
       "2018-03-26  168.07  173.10  166.44  172.770  36272617.0          0.0   \n",
       "2018-03-27  173.68  175.15  166.92  168.340  38962839.0          0.0   \n",
       "\n",
       "            Split Ratio  Adj. Open  Adj. High  Adj. Low  Adj. Close  \\\n",
       "Date                                                                  \n",
       "2018-03-21          1.0     175.04     175.09    171.26     171.270   \n",
       "2018-03-22          1.0     170.00     172.68    168.60     168.845   \n",
       "2018-03-23          1.0     168.39     169.92    164.94     164.940   \n",
       "2018-03-26          1.0     168.07     173.10    166.44     172.770   \n",
       "2018-03-27          1.0     173.68     175.15    166.92     168.340   \n",
       "\n",
       "            Adj. Volume     20d     50d    200d  20d-50d  \n",
       "Date                                                      \n",
       "2018-03-21   35247358.0  176.94  172.57  162.68     4.37  \n",
       "2018-03-22   41051076.0  176.76  172.46  162.75     4.30  \n",
       "2018-03-23   40248954.0  176.23  172.27  162.81     3.96  \n",
       "2018-03-26   36272617.0  175.92  172.22  162.91     3.70  \n",
       "2018-03-27   38962839.0  175.41  172.05  162.98     3.36  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple['20d-50d'] = apple['20d'] - apple['50d']\n",
    "apple.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x2108bb285c0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# np.where() is a vectorized if-else function, where a condition is checked for each component of a vector, and the first argument passed is used when the condition holds, and the other passed if it does not\n",
    "apple[\"Regime\"] = np.where(apple['20d-50d'] > 0, 1, 0)\n",
    "# We have 1's for bullish regimes and 0's for everything else. Below I replace bearish regimes's values with -1, and to maintain the rest of the vector, the second argument is apple[\"Regime\"]\n",
    "apple[\"Regime\"] = np.where(apple['20d-50d'] < 0, -1, apple[\"Regime\"])\n",
    "apple.loc['2016-01-04':'2016-12-31',\"Regime\"].plot(ylim = (-2,2)).axhline(y = 0, color = \"black\", lw = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x2108c7b0d30>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "apple[\"Regime\"].plot(ylim = (-2,2)).axhline(y = 0, color = \"black\", lw = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 1    1323\n",
       "-1     694\n",
       " 0      53\n",
       "Name: Regime, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple[\"Regime\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Derek\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:190: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n",
      "C:\\Users\\Derek\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: RuntimeWarning: invalid value encountered in sign\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Ex-Dividend</th>\n",
       "      <th>Split Ratio</th>\n",
       "      <th>Adj. Open</th>\n",
       "      <th>Adj. High</th>\n",
       "      <th>Adj. Low</th>\n",
       "      <th>Adj. Close</th>\n",
       "      <th>Adj. Volume</th>\n",
       "      <th>20d</th>\n",
       "      <th>50d</th>\n",
       "      <th>200d</th>\n",
       "      <th>20d-50d</th>\n",
       "      <th>Regime</th>\n",
       "      <th>Signal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-03-21</th>\n",
       "      <td>175.04</td>\n",
       "      <td>175.09</td>\n",
       "      <td>171.26</td>\n",
       "      <td>171.270</td>\n",
       "      <td>35247358.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>175.04</td>\n",
       "      <td>175.09</td>\n",
       "      <td>171.26</td>\n",
       "      <td>171.270</td>\n",
       "      <td>35247358.0</td>\n",
       "      <td>176.94</td>\n",
       "      <td>172.57</td>\n",
       "      <td>162.68</td>\n",
       "      <td>4.37</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-22</th>\n",
       "      <td>170.00</td>\n",
       "      <td>172.68</td>\n",
       "      <td>168.60</td>\n",
       "      <td>168.845</td>\n",
       "      <td>41051076.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>170.00</td>\n",
       "      <td>172.68</td>\n",
       "      <td>168.60</td>\n",
       "      <td>168.845</td>\n",
       "      <td>41051076.0</td>\n",
       "      <td>176.76</td>\n",
       "      <td>172.46</td>\n",
       "      <td>162.75</td>\n",
       "      <td>4.30</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-23</th>\n",
       "      <td>168.39</td>\n",
       "      <td>169.92</td>\n",
       "      <td>164.94</td>\n",
       "      <td>164.940</td>\n",
       "      <td>40248954.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>168.39</td>\n",
       "      <td>169.92</td>\n",
       "      <td>164.94</td>\n",
       "      <td>164.940</td>\n",
       "      <td>40248954.0</td>\n",
       "      <td>176.23</td>\n",
       "      <td>172.27</td>\n",
       "      <td>162.81</td>\n",
       "      <td>3.96</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-26</th>\n",
       "      <td>168.07</td>\n",
       "      <td>173.10</td>\n",
       "      <td>166.44</td>\n",
       "      <td>172.770</td>\n",
       "      <td>36272617.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>168.07</td>\n",
       "      <td>173.10</td>\n",
       "      <td>166.44</td>\n",
       "      <td>172.770</td>\n",
       "      <td>36272617.0</td>\n",
       "      <td>175.92</td>\n",
       "      <td>172.22</td>\n",
       "      <td>162.91</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-27</th>\n",
       "      <td>173.68</td>\n",
       "      <td>175.15</td>\n",
       "      <td>166.92</td>\n",
       "      <td>168.340</td>\n",
       "      <td>38962839.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>173.68</td>\n",
       "      <td>175.15</td>\n",
       "      <td>166.92</td>\n",
       "      <td>168.340</td>\n",
       "      <td>38962839.0</td>\n",
       "      <td>175.41</td>\n",
       "      <td>172.05</td>\n",
       "      <td>162.98</td>\n",
       "      <td>3.36</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low    Close      Volume  Ex-Dividend  \\\n",
       "Date                                                                   \n",
       "2018-03-21  175.04  175.09  171.26  171.270  35247358.0          0.0   \n",
       "2018-03-22  170.00  172.68  168.60  168.845  41051076.0          0.0   \n",
       "2018-03-23  168.39  169.92  164.94  164.940  40248954.0          0.0   \n",
       "2018-03-26  168.07  173.10  166.44  172.770  36272617.0          0.0   \n",
       "2018-03-27  173.68  175.15  166.92  168.340  38962839.0          0.0   \n",
       "\n",
       "            Split Ratio  Adj. Open  Adj. High  Adj. Low  Adj. Close  \\\n",
       "Date                                                                  \n",
       "2018-03-21          1.0     175.04     175.09    171.26     171.270   \n",
       "2018-03-22          1.0     170.00     172.68    168.60     168.845   \n",
       "2018-03-23          1.0     168.39     169.92    164.94     164.940   \n",
       "2018-03-26          1.0     168.07     173.10    166.44     172.770   \n",
       "2018-03-27          1.0     173.68     175.15    166.92     168.340   \n",
       "\n",
       "            Adj. Volume     20d     50d    200d  20d-50d  Regime  Signal  \n",
       "Date                                                                      \n",
       "2018-03-21   35247358.0  176.94  172.57  162.68     4.37       1     0.0  \n",
       "2018-03-22   41051076.0  176.76  172.46  162.75     4.30       1     0.0  \n",
       "2018-03-23   40248954.0  176.23  172.27  162.81     3.96       1     0.0  \n",
       "2018-03-26   36272617.0  175.92  172.22  162.91     3.70       1     0.0  \n",
       "2018-03-27   38962839.0  175.41  172.05  162.98     3.36       1    -1.0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To ensure that all trades close out, I temporarily change the regime of the last row to 0\n",
    "regime_orig = apple.loc[:, \"Regime\"].iloc[-1]\n",
    "apple.loc[:, \"Regime\"].iloc[-1] = 0\n",
    "apple[\"Signal\"] = np.sign(apple[\"Regime\"] - apple[\"Regime\"].shift(1))\n",
    "# Restore original regime data\n",
    "apple.loc[:, \"Regime\"].iloc[-1] = regime_orig\n",
    "apple.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2108c6cb1d0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "apple[\"Signal\"].plot(ylim = (-2, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0.0    2014\n",
       "-1.0      28\n",
       " 1.0      27\n",
       "Name: Signal, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple[\"Signal\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2010-03-16    224.450\n",
       "2010-06-18    274.074\n",
       "2010-08-16    247.640\n",
       "2010-09-20    283.230\n",
       "2011-05-12    346.570\n",
       "2011-07-14    357.770\n",
       "2011-12-28    402.640\n",
       "2012-06-25    570.765\n",
       "2013-05-17    433.260\n",
       "2013-07-31    452.530\n",
       "2013-10-16    501.114\n",
       "2014-03-11    536.090\n",
       "2014-03-12    536.610\n",
       "2014-03-24    539.190\n",
       "2014-04-25    571.940\n",
       "2014-10-28    106.740\n",
       "2015-02-05    119.940\n",
       "2015-04-28    130.560\n",
       "2015-10-27    114.550\n",
       "2016-03-10    101.170\n",
       "2016-06-23     96.100\n",
       "2016-06-30     95.600\n",
       "2016-07-25     97.340\n",
       "2016-12-21    117.060\n",
       "2017-08-02    157.140\n",
       "2017-11-01    166.890\n",
       "2018-03-08    176.940\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple.loc[apple[\"Signal\"] == 1, \"Close\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2010-06-11    253.5100\n",
       "2010-07-22    259.0240\n",
       "2010-08-17    251.9700\n",
       "2011-03-30    348.6300\n",
       "2011-03-31    348.5075\n",
       "2011-05-27    337.4100\n",
       "2011-11-17    377.4100\n",
       "2012-05-09    569.1800\n",
       "2012-10-17    644.6136\n",
       "2013-06-26    398.0700\n",
       "2013-10-04    483.0300\n",
       "2014-01-28    506.5000\n",
       "2014-03-17    526.7400\n",
       "2014-04-22    531.6990\n",
       "2014-10-17     97.6700\n",
       "2015-01-05    106.2500\n",
       "2015-04-16    126.1700\n",
       "2015-06-25    127.5000\n",
       "2015-06-26    126.7500\n",
       "2015-12-18    106.0300\n",
       "2016-05-05     93.2400\n",
       "2016-06-27     92.0400\n",
       "2016-07-11     96.9800\n",
       "2016-11-15    107.1100\n",
       "2017-06-27    143.7400\n",
       "2017-10-03    154.4800\n",
       "2018-02-06    163.0300\n",
       "2018-03-27    168.3400\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple.loc[apple[\"Signal\"] == -1, \"Close\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Regime</th>\n",
       "      <th>Signal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-03-16</th>\n",
       "      <td>28.844953</td>\n",
       "      <td>1</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-11</th>\n",
       "      <td>32.579568</td>\n",
       "      <td>-1</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-18</th>\n",
       "      <td>35.222329</td>\n",
       "      <td>1</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-22</th>\n",
       "      <td>33.288194</td>\n",
       "      <td>-1</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-16</th>\n",
       "      <td>31.825192</td>\n",
       "      <td>0</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-17</th>\n",
       "      <td>32.381657</td>\n",
       "      <td>-1</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-09-20</th>\n",
       "      <td>36.399003</td>\n",
       "      <td>1</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03-30</th>\n",
       "      <td>44.803814</td>\n",
       "      <td>0</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03-31</th>\n",
       "      <td>44.788071</td>\n",
       "      <td>-1</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-12</th>\n",
       "      <td>44.539075</td>\n",
       "      <td>1</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-27</th>\n",
       "      <td>43.361888</td>\n",
       "      <td>-1</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-14</th>\n",
       "      <td>45.978431</td>\n",
       "      <td>1</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-11-17</th>\n",
       "      <td>48.502445</td>\n",
       "      <td>-1</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-28</th>\n",
       "      <td>51.744852</td>\n",
       "      <td>1</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-05-09</th>\n",
       "      <td>73.147563</td>\n",
       "      <td>-1</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-25</th>\n",
       "      <td>73.351258</td>\n",
       "      <td>1</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-17</th>\n",
       "      <td>83.195498</td>\n",
       "      <td>-1</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-17</th>\n",
       "      <td>56.878472</td>\n",
       "      <td>1</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-06-26</th>\n",
       "      <td>52.258721</td>\n",
       "      <td>-1</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-07-31</th>\n",
       "      <td>59.408242</td>\n",
       "      <td>1</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-04</th>\n",
       "      <td>63.831819</td>\n",
       "      <td>-1</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-16</th>\n",
       "      <td>66.221597</td>\n",
       "      <td>1</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-28</th>\n",
       "      <td>67.325247</td>\n",
       "      <td>-1</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-11</th>\n",
       "      <td>71.682490</td>\n",
       "      <td>0</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-12</th>\n",
       "      <td>71.752021</td>\n",
       "      <td>1</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-17</th>\n",
       "      <td>70.432269</td>\n",
       "      <td>-1</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-24</th>\n",
       "      <td>72.097002</td>\n",
       "      <td>1</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-22</th>\n",
       "      <td>71.095354</td>\n",
       "      <td>-1</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-25</th>\n",
       "      <td>76.476120</td>\n",
       "      <td>1</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-17</th>\n",
       "      <td>92.387441</td>\n",
       "      <td>-1</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-28</th>\n",
       "      <td>100.966883</td>\n",
       "      <td>1</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>100.937944</td>\n",
       "      <td>-1</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-05</th>\n",
       "      <td>114.390004</td>\n",
       "      <td>1</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-16</th>\n",
       "      <td>120.331722</td>\n",
       "      <td>-1</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-28</th>\n",
       "      <td>124.518583</td>\n",
       "      <td>1</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-25</th>\n",
       "      <td>122.104986</td>\n",
       "      <td>0</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-26</th>\n",
       "      <td>121.386721</td>\n",
       "      <td>-1</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-27</th>\n",
       "      <td>110.198438</td>\n",
       "      <td>1</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-18</th>\n",
       "      <td>102.440744</td>\n",
       "      <td>-1</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-10</th>\n",
       "      <td>98.271427</td>\n",
       "      <td>1</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-05</th>\n",
       "      <td>91.122295</td>\n",
       "      <td>-1</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-23</th>\n",
       "      <td>93.917337</td>\n",
       "      <td>1</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-27</th>\n",
       "      <td>89.949550</td>\n",
       "      <td>-1</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-30</th>\n",
       "      <td>93.428693</td>\n",
       "      <td>1</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-11</th>\n",
       "      <td>94.777350</td>\n",
       "      <td>-1</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-25</th>\n",
       "      <td>95.129174</td>\n",
       "      <td>1</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-15</th>\n",
       "      <td>105.787035</td>\n",
       "      <td>-1</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-21</th>\n",
       "      <td>115.614138</td>\n",
       "      <td>1</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-27</th>\n",
       "      <td>143.159139</td>\n",
       "      <td>-1</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-02</th>\n",
       "      <td>156.504989</td>\n",
       "      <td>1</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-03</th>\n",
       "      <td>154.480000</td>\n",
       "      <td>-1</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01</th>\n",
       "      <td>166.890000</td>\n",
       "      <td>1</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-06</th>\n",
       "      <td>163.030000</td>\n",
       "      <td>-1</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-08</th>\n",
       "      <td>176.940000</td>\n",
       "      <td>1</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-27</th>\n",
       "      <td>168.340000</td>\n",
       "      <td>1</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Price  Regime Signal\n",
       "Date                                 \n",
       "2010-03-16   28.844953       1    Buy\n",
       "2010-06-11   32.579568      -1   Sell\n",
       "2010-06-18   35.222329       1    Buy\n",
       "2010-07-22   33.288194      -1   Sell\n",
       "2010-08-16   31.825192       0    Buy\n",
       "2010-08-17   32.381657      -1   Sell\n",
       "2010-09-20   36.399003       1    Buy\n",
       "2011-03-30   44.803814       0   Sell\n",
       "2011-03-31   44.788071      -1   Sell\n",
       "2011-05-12   44.539075       1    Buy\n",
       "2011-05-27   43.361888      -1   Sell\n",
       "2011-07-14   45.978431       1    Buy\n",
       "2011-11-17   48.502445      -1   Sell\n",
       "2011-12-28   51.744852       1    Buy\n",
       "2012-05-09   73.147563      -1   Sell\n",
       "2012-06-25   73.351258       1    Buy\n",
       "2012-10-17   83.195498      -1   Sell\n",
       "2013-05-17   56.878472       1    Buy\n",
       "2013-06-26   52.258721      -1   Sell\n",
       "2013-07-31   59.408242       1    Buy\n",
       "2013-10-04   63.831819      -1   Sell\n",
       "2013-10-16   66.221597       1    Buy\n",
       "2014-01-28   67.325247      -1   Sell\n",
       "2014-03-11   71.682490       0    Buy\n",
       "2014-03-12   71.752021       1    Buy\n",
       "2014-03-17   70.432269      -1   Sell\n",
       "2014-03-24   72.097002       1    Buy\n",
       "2014-04-22   71.095354      -1   Sell\n",
       "2014-04-25   76.476120       1    Buy\n",
       "2014-10-17   92.387441      -1   Sell\n",
       "2014-10-28  100.966883       1    Buy\n",
       "2015-01-05  100.937944      -1   Sell\n",
       "2015-02-05  114.390004       1    Buy\n",
       "2015-04-16  120.331722      -1   Sell\n",
       "2015-04-28  124.518583       1    Buy\n",
       "2015-06-25  122.104986       0   Sell\n",
       "2015-06-26  121.386721      -1   Sell\n",
       "2015-10-27  110.198438       1    Buy\n",
       "2015-12-18  102.440744      -1   Sell\n",
       "2016-03-10   98.271427       1    Buy\n",
       "2016-05-05   91.122295      -1   Sell\n",
       "2016-06-23   93.917337       1    Buy\n",
       "2016-06-27   89.949550      -1   Sell\n",
       "2016-06-30   93.428693       1    Buy\n",
       "2016-07-11   94.777350      -1   Sell\n",
       "2016-07-25   95.129174       1    Buy\n",
       "2016-11-15  105.787035      -1   Sell\n",
       "2016-12-21  115.614138       1    Buy\n",
       "2017-06-27  143.159139      -1   Sell\n",
       "2017-08-02  156.504989       1    Buy\n",
       "2017-10-03  154.480000      -1   Sell\n",
       "2017-11-01  166.890000       1    Buy\n",
       "2018-02-06  163.030000      -1   Sell\n",
       "2018-03-08  176.940000       1    Buy\n",
       "2018-03-27  168.340000       1   Sell"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple_signals = pd.concat([\n",
    "        pd.DataFrame({\"Price\": apple.loc[apple[\"Signal\"] == 1, \"Adj. Close\"],\n",
    "                     \"Regime\": apple.loc[apple[\"Signal\"] == 1, \"Regime\"],\n",
    "                     \"Signal\": \"Buy\"}),\n",
    "        pd.DataFrame({\"Price\": apple.loc[apple[\"Signal\"] == -1, \"Adj. Close\"],\n",
    "                     \"Regime\": apple.loc[apple[\"Signal\"] == -1, \"Regime\"],\n",
    "                     \"Signal\": \"Sell\"}),\n",
    "    ])\n",
    "apple_signals.sort_index(inplace = True)\n",
    "apple_signals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Profit</th>\n",
       "      <th>End Date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-03-16</th>\n",
       "      <td>28.844953</td>\n",
       "      <td>3.734615</td>\n",
       "      <td>2010-06-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-18</th>\n",
       "      <td>35.222329</td>\n",
       "      <td>-1.934135</td>\n",
       "      <td>2010-07-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-09-20</th>\n",
       "      <td>36.399003</td>\n",
       "      <td>8.404812</td>\n",
       "      <td>2011-03-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-12</th>\n",
       "      <td>44.539075</td>\n",
       "      <td>-1.177188</td>\n",
       "      <td>2011-05-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-14</th>\n",
       "      <td>45.978431</td>\n",
       "      <td>2.524014</td>\n",
       "      <td>2011-11-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-28</th>\n",
       "      <td>51.744852</td>\n",
       "      <td>21.402711</td>\n",
       "      <td>2012-05-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-25</th>\n",
       "      <td>73.351258</td>\n",
       "      <td>9.844240</td>\n",
       "      <td>2012-10-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-17</th>\n",
       "      <td>56.878472</td>\n",
       "      <td>-4.619751</td>\n",
       "      <td>2013-06-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-07-31</th>\n",
       "      <td>59.408242</td>\n",
       "      <td>4.423577</td>\n",
       "      <td>2013-10-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-16</th>\n",
       "      <td>66.221597</td>\n",
       "      <td>1.103650</td>\n",
       "      <td>2014-01-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-12</th>\n",
       "      <td>71.752021</td>\n",
       "      <td>-1.319753</td>\n",
       "      <td>2014-03-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-24</th>\n",
       "      <td>72.097002</td>\n",
       "      <td>-1.001648</td>\n",
       "      <td>2014-04-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-25</th>\n",
       "      <td>76.476120</td>\n",
       "      <td>15.911321</td>\n",
       "      <td>2014-10-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-28</th>\n",
       "      <td>100.966883</td>\n",
       "      <td>-0.028939</td>\n",
       "      <td>2015-01-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-05</th>\n",
       "      <td>114.390004</td>\n",
       "      <td>5.941719</td>\n",
       "      <td>2015-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-28</th>\n",
       "      <td>124.518583</td>\n",
       "      <td>-2.413598</td>\n",
       "      <td>2015-06-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-27</th>\n",
       "      <td>110.198438</td>\n",
       "      <td>-7.757693</td>\n",
       "      <td>2015-12-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-10</th>\n",
       "      <td>98.271427</td>\n",
       "      <td>-7.149132</td>\n",
       "      <td>2016-05-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-23</th>\n",
       "      <td>93.917337</td>\n",
       "      <td>-3.967788</td>\n",
       "      <td>2016-06-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-30</th>\n",
       "      <td>93.428693</td>\n",
       "      <td>1.348657</td>\n",
       "      <td>2016-07-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-25</th>\n",
       "      <td>95.129174</td>\n",
       "      <td>10.657861</td>\n",
       "      <td>2016-11-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-21</th>\n",
       "      <td>115.614138</td>\n",
       "      <td>27.545001</td>\n",
       "      <td>2017-06-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-02</th>\n",
       "      <td>156.504989</td>\n",
       "      <td>-2.024989</td>\n",
       "      <td>2017-10-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01</th>\n",
       "      <td>166.890000</td>\n",
       "      <td>-3.860000</td>\n",
       "      <td>2018-02-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-08</th>\n",
       "      <td>176.940000</td>\n",
       "      <td>-8.600000</td>\n",
       "      <td>2018-03-27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Price     Profit   End Date\n",
       "Date                                        \n",
       "2010-03-16   28.844953   3.734615 2010-06-11\n",
       "2010-06-18   35.222329  -1.934135 2010-07-22\n",
       "2010-09-20   36.399003   8.404812 2011-03-30\n",
       "2011-05-12   44.539075  -1.177188 2011-05-27\n",
       "2011-07-14   45.978431   2.524014 2011-11-17\n",
       "2011-12-28   51.744852  21.402711 2012-05-09\n",
       "2012-06-25   73.351258   9.844240 2012-10-17\n",
       "2013-05-17   56.878472  -4.619751 2013-06-26\n",
       "2013-07-31   59.408242   4.423577 2013-10-04\n",
       "2013-10-16   66.221597   1.103650 2014-01-28\n",
       "2014-03-12   71.752021  -1.319753 2014-03-17\n",
       "2014-03-24   72.097002  -1.001648 2014-04-22\n",
       "2014-04-25   76.476120  15.911321 2014-10-17\n",
       "2014-10-28  100.966883  -0.028939 2015-01-05\n",
       "2015-02-05  114.390004   5.941719 2015-04-16\n",
       "2015-04-28  124.518583  -2.413598 2015-06-25\n",
       "2015-10-27  110.198438  -7.757693 2015-12-18\n",
       "2016-03-10   98.271427  -7.149132 2016-05-05\n",
       "2016-06-23   93.917337  -3.967788 2016-06-27\n",
       "2016-06-30   93.428693   1.348657 2016-07-11\n",
       "2016-07-25   95.129174  10.657861 2016-11-15\n",
       "2016-12-21  115.614138  27.545001 2017-06-27\n",
       "2017-08-02  156.504989  -2.024989 2017-10-03\n",
       "2017-11-01  166.890000  -3.860000 2018-02-06\n",
       "2018-03-08  176.940000  -8.600000 2018-03-27"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's see the profitability of long trades\n",
    "apple_long_profits = pd.DataFrame({\n",
    "        \"Price\": apple_signals.loc[(apple_signals[\"Signal\"] == \"Buy\") &\n",
    "                                  apple_signals[\"Regime\"] == 1, \"Price\"],\n",
    "        \"Profit\": pd.Series(apple_signals[\"Price\"] - apple_signals[\"Price\"].shift(1)).loc[\n",
    "            apple_signals.loc[(apple_signals[\"Signal\"].shift(1) == \"Buy\") & (apple_signals[\"Regime\"].shift(1) == 1)].index\n",
    "        ].tolist(),\n",
    "        \"End Date\": apple_signals[\"Price\"].loc[\n",
    "            apple_signals.loc[(apple_signals[\"Signal\"].shift(1) == \"Buy\") & (apple_signals[\"Regime\"].shift(1) == 1)].index\n",
    "        ].index\n",
    "    })\n",
    "apple_long_profits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Profit</th>\n",
       "      <th>End Date</th>\n",
       "      <th>Low</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-03-16</th>\n",
       "      <td>28.844953</td>\n",
       "      <td>3.734615</td>\n",
       "      <td>2010-06-11</td>\n",
       "      <td>25.606402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-18</th>\n",
       "      <td>35.222329</td>\n",
       "      <td>-1.934135</td>\n",
       "      <td>2010-07-22</td>\n",
       "      <td>30.791939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-09-20</th>\n",
       "      <td>36.399003</td>\n",
       "      <td>8.404812</td>\n",
       "      <td>2011-03-30</td>\n",
       "      <td>35.341333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-12</th>\n",
       "      <td>44.539075</td>\n",
       "      <td>-1.177188</td>\n",
       "      <td>2011-05-27</td>\n",
       "      <td>42.335061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-14</th>\n",
       "      <td>45.978431</td>\n",
       "      <td>2.524014</td>\n",
       "      <td>2011-11-17</td>\n",
       "      <td>45.367990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-28</th>\n",
       "      <td>51.744852</td>\n",
       "      <td>21.402711</td>\n",
       "      <td>2012-05-09</td>\n",
       "      <td>51.471117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-25</th>\n",
       "      <td>73.351258</td>\n",
       "      <td>9.844240</td>\n",
       "      <td>2012-10-17</td>\n",
       "      <td>72.688768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-17</th>\n",
       "      <td>56.878472</td>\n",
       "      <td>-4.619751</td>\n",
       "      <td>2013-06-26</td>\n",
       "      <td>51.942335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-07-31</th>\n",
       "      <td>59.408242</td>\n",
       "      <td>4.423577</td>\n",
       "      <td>2013-10-04</td>\n",
       "      <td>59.001273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-16</th>\n",
       "      <td>66.221597</td>\n",
       "      <td>1.103650</td>\n",
       "      <td>2014-01-28</td>\n",
       "      <td>65.972629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-12</th>\n",
       "      <td>71.752021</td>\n",
       "      <td>-1.319753</td>\n",
       "      <td>2014-03-17</td>\n",
       "      <td>69.932180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-24</th>\n",
       "      <td>72.097002</td>\n",
       "      <td>-1.001648</td>\n",
       "      <td>2014-04-22</td>\n",
       "      <td>68.371743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-25</th>\n",
       "      <td>76.476120</td>\n",
       "      <td>15.911321</td>\n",
       "      <td>2014-10-17</td>\n",
       "      <td>75.409086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-28</th>\n",
       "      <td>100.966883</td>\n",
       "      <td>-0.028939</td>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>99.652062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-05</th>\n",
       "      <td>114.390004</td>\n",
       "      <td>5.941719</td>\n",
       "      <td>2015-04-16</td>\n",
       "      <td>112.949876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-28</th>\n",
       "      <td>124.518583</td>\n",
       "      <td>-2.413598</td>\n",
       "      <td>2015-06-25</td>\n",
       "      <td>117.651750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-27</th>\n",
       "      <td>110.198438</td>\n",
       "      <td>-7.757693</td>\n",
       "      <td>2015-12-18</td>\n",
       "      <td>102.228192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-10</th>\n",
       "      <td>98.271427</td>\n",
       "      <td>-7.149132</td>\n",
       "      <td>2016-05-05</td>\n",
       "      <td>89.752692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-23</th>\n",
       "      <td>93.917337</td>\n",
       "      <td>-3.967788</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>89.421814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-30</th>\n",
       "      <td>93.428693</td>\n",
       "      <td>1.348657</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>92.158220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-25</th>\n",
       "      <td>95.129174</td>\n",
       "      <td>10.657861</td>\n",
       "      <td>2016-11-15</td>\n",
       "      <td>94.230069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-21</th>\n",
       "      <td>115.614138</td>\n",
       "      <td>27.545001</td>\n",
       "      <td>2017-06-27</td>\n",
       "      <td>113.342546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-02</th>\n",
       "      <td>156.504989</td>\n",
       "      <td>-2.024989</td>\n",
       "      <td>2017-10-03</td>\n",
       "      <td>149.160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01</th>\n",
       "      <td>166.890000</td>\n",
       "      <td>-3.860000</td>\n",
       "      <td>2018-02-06</td>\n",
       "      <td>154.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-08</th>\n",
       "      <td>176.940000</td>\n",
       "      <td>-8.600000</td>\n",
       "      <td>2018-03-27</td>\n",
       "      <td>164.940000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Price     Profit   End Date         Low\n",
       "Date                                                    \n",
       "2010-03-16   28.844953   3.734615 2010-06-11   25.606402\n",
       "2010-06-18   35.222329  -1.934135 2010-07-22   30.791939\n",
       "2010-09-20   36.399003   8.404812 2011-03-30   35.341333\n",
       "2011-05-12   44.539075  -1.177188 2011-05-27   42.335061\n",
       "2011-07-14   45.978431   2.524014 2011-11-17   45.367990\n",
       "2011-12-28   51.744852  21.402711 2012-05-09   51.471117\n",
       "2012-06-25   73.351258   9.844240 2012-10-17   72.688768\n",
       "2013-05-17   56.878472  -4.619751 2013-06-26   51.942335\n",
       "2013-07-31   59.408242   4.423577 2013-10-04   59.001273\n",
       "2013-10-16   66.221597   1.103650 2014-01-28   65.972629\n",
       "2014-03-12   71.752021  -1.319753 2014-03-17   69.932180\n",
       "2014-03-24   72.097002  -1.001648 2014-04-22   68.371743\n",
       "2014-04-25   76.476120  15.911321 2014-10-17   75.409086\n",
       "2014-10-28  100.966883  -0.028939 2015-01-05   99.652062\n",
       "2015-02-05  114.390004   5.941719 2015-04-16  112.949876\n",
       "2015-04-28  124.518583  -2.413598 2015-06-25  117.651750\n",
       "2015-10-27  110.198438  -7.757693 2015-12-18  102.228192\n",
       "2016-03-10   98.271427  -7.149132 2016-05-05   89.752692\n",
       "2016-06-23   93.917337  -3.967788 2016-06-27   89.421814\n",
       "2016-06-30   93.428693   1.348657 2016-07-11   92.158220\n",
       "2016-07-25   95.129174  10.657861 2016-11-15   94.230069\n",
       "2016-12-21  115.614138  27.545001 2017-06-27  113.342546\n",
       "2017-08-02  156.504989  -2.024989 2017-10-03  149.160000\n",
       "2017-11-01  166.890000  -3.860000 2018-02-06  154.000000\n",
       "2018-03-08  176.940000  -8.600000 2018-03-27  164.940000"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We need to get the low of the price during each trade.\n",
    "tradeperiods = pd.DataFrame({\"Start\": apple_long_profits.index,\n",
    "                            \"End\": apple_long_profits[\"End Date\"]})\n",
    "apple_long_profits[\"Low\"] = tradeperiods.apply(lambda x: min(apple.loc[x[\"Start\"]:x[\"End\"], \"Adj. Low\"]), axis = 1)\n",
    "apple_long_profits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Start Port. Value</th>\n",
       "      <th>End Port. Value</th>\n",
       "      <th>End Date</th>\n",
       "      <th>Shares</th>\n",
       "      <th>Share Price</th>\n",
       "      <th>Trade Value</th>\n",
       "      <th>Profit per Share</th>\n",
       "      <th>Total Profit</th>\n",
       "      <th>Stop-Loss Triggered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-03-16</th>\n",
       "      <td>1.000000e+06</td>\n",
       "      <td>1.012698e+06</td>\n",
       "      <td>2010-06-11</td>\n",
       "      <td>3400.0</td>\n",
       "      <td>28.844953</td>\n",
       "      <td>98072.841239</td>\n",
       "      <td>3.734615</td>\n",
       "      <td>12697.691096</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-18</th>\n",
       "      <td>1.012698e+06</td>\n",
       "      <td>1.007282e+06</td>\n",
       "      <td>2010-07-22</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>35.222329</td>\n",
       "      <td>98622.521053</td>\n",
       "      <td>-1.934135</td>\n",
       "      <td>-5415.577333</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-09-20</th>\n",
       "      <td>1.007282e+06</td>\n",
       "      <td>1.029975e+06</td>\n",
       "      <td>2011-03-30</td>\n",
       "      <td>2700.0</td>\n",
       "      <td>36.399003</td>\n",
       "      <td>98277.306914</td>\n",
       "      <td>8.404812</td>\n",
       "      <td>22692.991110</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-12</th>\n",
       "      <td>1.029975e+06</td>\n",
       "      <td>1.027268e+06</td>\n",
       "      <td>2011-05-27</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>44.539075</td>\n",
       "      <td>102439.873355</td>\n",
       "      <td>-1.177188</td>\n",
       "      <td>-2707.531638</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-14</th>\n",
       "      <td>1.027268e+06</td>\n",
       "      <td>1.032820e+06</td>\n",
       "      <td>2011-11-17</td>\n",
       "      <td>2200.0</td>\n",
       "      <td>45.978431</td>\n",
       "      <td>101152.549241</td>\n",
       "      <td>2.524014</td>\n",
       "      <td>5552.830218</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-28</th>\n",
       "      <td>1.032820e+06</td>\n",
       "      <td>1.073486e+06</td>\n",
       "      <td>2012-05-09</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>51.744852</td>\n",
       "      <td>98315.218526</td>\n",
       "      <td>21.402711</td>\n",
       "      <td>40665.151235</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-25</th>\n",
       "      <td>1.073486e+06</td>\n",
       "      <td>1.087267e+06</td>\n",
       "      <td>2012-10-17</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>73.351258</td>\n",
       "      <td>102691.760672</td>\n",
       "      <td>9.844240</td>\n",
       "      <td>13781.935982</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-17</th>\n",
       "      <td>1.087267e+06</td>\n",
       "      <td>1.078490e+06</td>\n",
       "      <td>2013-06-26</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>56.878472</td>\n",
       "      <td>108069.096937</td>\n",
       "      <td>-4.619751</td>\n",
       "      <td>-8777.527400</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-07-31</th>\n",
       "      <td>1.078490e+06</td>\n",
       "      <td>1.086452e+06</td>\n",
       "      <td>2013-10-04</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>59.408242</td>\n",
       "      <td>106934.835757</td>\n",
       "      <td>4.423577</td>\n",
       "      <td>7962.438409</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-16</th>\n",
       "      <td>1.086452e+06</td>\n",
       "      <td>1.088218e+06</td>\n",
       "      <td>2014-01-28</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>66.221597</td>\n",
       "      <td>105954.555657</td>\n",
       "      <td>1.103650</td>\n",
       "      <td>1765.839598</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-12</th>\n",
       "      <td>1.088218e+06</td>\n",
       "      <td>1.086239e+06</td>\n",
       "      <td>2014-03-17</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>71.752021</td>\n",
       "      <td>107628.031714</td>\n",
       "      <td>-1.319753</td>\n",
       "      <td>-1979.628917</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-24</th>\n",
       "      <td>1.086239e+06</td>\n",
       "      <td>1.084736e+06</td>\n",
       "      <td>2014-04-22</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>72.097002</td>\n",
       "      <td>108145.503103</td>\n",
       "      <td>-1.001648</td>\n",
       "      <td>-1502.472160</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-25</th>\n",
       "      <td>1.084736e+06</td>\n",
       "      <td>1.107012e+06</td>\n",
       "      <td>2014-10-17</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>76.476120</td>\n",
       "      <td>107066.568572</td>\n",
       "      <td>15.911321</td>\n",
       "      <td>22275.849051</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-28</th>\n",
       "      <td>1.107012e+06</td>\n",
       "      <td>1.106983e+06</td>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>100.966883</td>\n",
       "      <td>100966.883069</td>\n",
       "      <td>-0.028939</td>\n",
       "      <td>-28.938709</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-05</th>\n",
       "      <td>1.106983e+06</td>\n",
       "      <td>1.112331e+06</td>\n",
       "      <td>2015-04-16</td>\n",
       "      <td>900.0</td>\n",
       "      <td>114.390004</td>\n",
       "      <td>102951.003221</td>\n",
       "      <td>5.941719</td>\n",
       "      <td>5347.546691</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-28</th>\n",
       "      <td>1.112331e+06</td>\n",
       "      <td>1.110400e+06</td>\n",
       "      <td>2015-06-25</td>\n",
       "      <td>800.0</td>\n",
       "      <td>124.518583</td>\n",
       "      <td>99614.866549</td>\n",
       "      <td>-2.413598</td>\n",
       "      <td>-1930.878038</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-27</th>\n",
       "      <td>1.110400e+06</td>\n",
       "      <td>1.102642e+06</td>\n",
       "      <td>2015-12-18</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>110.198438</td>\n",
       "      <td>110198.437846</td>\n",
       "      <td>-7.757693</td>\n",
       "      <td>-7757.693367</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-10</th>\n",
       "      <td>1.102642e+06</td>\n",
       "      <td>1.094778e+06</td>\n",
       "      <td>2016-05-05</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>98.271427</td>\n",
       "      <td>108098.569555</td>\n",
       "      <td>-7.149132</td>\n",
       "      <td>-7864.045388</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-23</th>\n",
       "      <td>1.094778e+06</td>\n",
       "      <td>1.090413e+06</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>93.917337</td>\n",
       "      <td>103309.070918</td>\n",
       "      <td>-3.967788</td>\n",
       "      <td>-4364.566368</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-30</th>\n",
       "      <td>1.090413e+06</td>\n",
       "      <td>1.091897e+06</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>93.428693</td>\n",
       "      <td>102771.562745</td>\n",
       "      <td>1.348657</td>\n",
       "      <td>1483.522558</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-25</th>\n",
       "      <td>1.091897e+06</td>\n",
       "      <td>1.103621e+06</td>\n",
       "      <td>2016-11-15</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>95.129174</td>\n",
       "      <td>104642.091188</td>\n",
       "      <td>10.657861</td>\n",
       "      <td>11723.647322</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-21</th>\n",
       "      <td>1.103621e+06</td>\n",
       "      <td>1.128411e+06</td>\n",
       "      <td>2017-06-27</td>\n",
       "      <td>900.0</td>\n",
       "      <td>115.614138</td>\n",
       "      <td>104052.724175</td>\n",
       "      <td>27.545001</td>\n",
       "      <td>24790.501098</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-02</th>\n",
       "      <td>1.128411e+06</td>\n",
       "      <td>1.126994e+06</td>\n",
       "      <td>2017-10-03</td>\n",
       "      <td>700.0</td>\n",
       "      <td>156.504989</td>\n",
       "      <td>109553.492367</td>\n",
       "      <td>-2.024989</td>\n",
       "      <td>-1417.492367</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01</th>\n",
       "      <td>1.126994e+06</td>\n",
       "      <td>1.124678e+06</td>\n",
       "      <td>2018-02-06</td>\n",
       "      <td>600.0</td>\n",
       "      <td>166.890000</td>\n",
       "      <td>100134.000000</td>\n",
       "      <td>-3.860000</td>\n",
       "      <td>-2316.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-08</th>\n",
       "      <td>1.124678e+06</td>\n",
       "      <td>1.119518e+06</td>\n",
       "      <td>2018-03-27</td>\n",
       "      <td>600.0</td>\n",
       "      <td>176.940000</td>\n",
       "      <td>106164.000000</td>\n",
       "      <td>-8.600000</td>\n",
       "      <td>-5160.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Start Port. Value  End Port. Value   End Date  Shares  \\\n",
       "2010-03-16       1.000000e+06     1.012698e+06 2010-06-11  3400.0   \n",
       "2010-06-18       1.012698e+06     1.007282e+06 2010-07-22  2800.0   \n",
       "2010-09-20       1.007282e+06     1.029975e+06 2011-03-30  2700.0   \n",
       "2011-05-12       1.029975e+06     1.027268e+06 2011-05-27  2300.0   \n",
       "2011-07-14       1.027268e+06     1.032820e+06 2011-11-17  2200.0   \n",
       "2011-12-28       1.032820e+06     1.073486e+06 2012-05-09  1900.0   \n",
       "2012-06-25       1.073486e+06     1.087267e+06 2012-10-17  1400.0   \n",
       "2013-05-17       1.087267e+06     1.078490e+06 2013-06-26  1900.0   \n",
       "2013-07-31       1.078490e+06     1.086452e+06 2013-10-04  1800.0   \n",
       "2013-10-16       1.086452e+06     1.088218e+06 2014-01-28  1600.0   \n",
       "2014-03-12       1.088218e+06     1.086239e+06 2014-03-17  1500.0   \n",
       "2014-03-24       1.086239e+06     1.084736e+06 2014-04-22  1500.0   \n",
       "2014-04-25       1.084736e+06     1.107012e+06 2014-10-17  1400.0   \n",
       "2014-10-28       1.107012e+06     1.106983e+06 2015-01-05  1000.0   \n",
       "2015-02-05       1.106983e+06     1.112331e+06 2015-04-16   900.0   \n",
       "2015-04-28       1.112331e+06     1.110400e+06 2015-06-25   800.0   \n",
       "2015-10-27       1.110400e+06     1.102642e+06 2015-12-18  1000.0   \n",
       "2016-03-10       1.102642e+06     1.094778e+06 2016-05-05  1100.0   \n",
       "2016-06-23       1.094778e+06     1.090413e+06 2016-06-27  1100.0   \n",
       "2016-06-30       1.090413e+06     1.091897e+06 2016-07-11  1100.0   \n",
       "2016-07-25       1.091897e+06     1.103621e+06 2016-11-15  1100.0   \n",
       "2016-12-21       1.103621e+06     1.128411e+06 2017-06-27   900.0   \n",
       "2017-08-02       1.128411e+06     1.126994e+06 2017-10-03   700.0   \n",
       "2017-11-01       1.126994e+06     1.124678e+06 2018-02-06   600.0   \n",
       "2018-03-08       1.124678e+06     1.119518e+06 2018-03-27   600.0   \n",
       "\n",
       "            Share Price    Trade Value  Profit per Share  Total Profit  \\\n",
       "2010-03-16    28.844953   98072.841239          3.734615  12697.691096   \n",
       "2010-06-18    35.222329   98622.521053         -1.934135  -5415.577333   \n",
       "2010-09-20    36.399003   98277.306914          8.404812  22692.991110   \n",
       "2011-05-12    44.539075  102439.873355         -1.177188  -2707.531638   \n",
       "2011-07-14    45.978431  101152.549241          2.524014   5552.830218   \n",
       "2011-12-28    51.744852   98315.218526         21.402711  40665.151235   \n",
       "2012-06-25    73.351258  102691.760672          9.844240  13781.935982   \n",
       "2013-05-17    56.878472  108069.096937         -4.619751  -8777.527400   \n",
       "2013-07-31    59.408242  106934.835757          4.423577   7962.438409   \n",
       "2013-10-16    66.221597  105954.555657          1.103650   1765.839598   \n",
       "2014-03-12    71.752021  107628.031714         -1.319753  -1979.628917   \n",
       "2014-03-24    72.097002  108145.503103         -1.001648  -1502.472160   \n",
       "2014-04-25    76.476120  107066.568572         15.911321  22275.849051   \n",
       "2014-10-28   100.966883  100966.883069         -0.028939    -28.938709   \n",
       "2015-02-05   114.390004  102951.003221          5.941719   5347.546691   \n",
       "2015-04-28   124.518583   99614.866549         -2.413598  -1930.878038   \n",
       "2015-10-27   110.198438  110198.437846         -7.757693  -7757.693367   \n",
       "2016-03-10    98.271427  108098.569555         -7.149132  -7864.045388   \n",
       "2016-06-23    93.917337  103309.070918         -3.967788  -4364.566368   \n",
       "2016-06-30    93.428693  102771.562745          1.348657   1483.522558   \n",
       "2016-07-25    95.129174  104642.091188         10.657861  11723.647322   \n",
       "2016-12-21   115.614138  104052.724175         27.545001  24790.501098   \n",
       "2017-08-02   156.504989  109553.492367         -2.024989  -1417.492367   \n",
       "2017-11-01   166.890000  100134.000000         -3.860000  -2316.000000   \n",
       "2018-03-08   176.940000  106164.000000         -8.600000  -5160.000000   \n",
       "\n",
       "            Stop-Loss Triggered  \n",
       "2010-03-16                  0.0  \n",
       "2010-06-18                  0.0  \n",
       "2010-09-20                  0.0  \n",
       "2011-05-12                  0.0  \n",
       "2011-07-14                  0.0  \n",
       "2011-12-28                  0.0  \n",
       "2012-06-25                  0.0  \n",
       "2013-05-17                  0.0  \n",
       "2013-07-31                  0.0  \n",
       "2013-10-16                  0.0  \n",
       "2014-03-12                  0.0  \n",
       "2014-03-24                  0.0  \n",
       "2014-04-25                  0.0  \n",
       "2014-10-28                  0.0  \n",
       "2015-02-05                  0.0  \n",
       "2015-04-28                  0.0  \n",
       "2015-10-27                  0.0  \n",
       "2016-03-10                  0.0  \n",
       "2016-06-23                  0.0  \n",
       "2016-06-30                  0.0  \n",
       "2016-07-25                  0.0  \n",
       "2016-12-21                  0.0  \n",
       "2017-08-02                  0.0  \n",
       "2017-11-01                  0.0  \n",
       "2018-03-08                  0.0  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now we have all the information needed to simulate this strategy in apple_adj_long_profits\n",
    "cash = 1000000\n",
    "apple_backtest = pd.DataFrame({\"Start Port. Value\": [],\n",
    "                         \"End Port. Value\": [],\n",
    "                         \"End Date\": [],\n",
    "                         \"Shares\": [],\n",
    "                         \"Share Price\": [],\n",
    "                         \"Trade Value\": [],\n",
    "                         \"Profit per Share\": [],\n",
    "                         \"Total Profit\": [],\n",
    "                         \"Stop-Loss Triggered\": []})\n",
    "port_value = .1  # Max proportion of portfolio bet on any trade\n",
    "batch = 100      # Number of shares bought per batch\n",
    "stoploss = .2    # % of trade loss that would trigger a stoploss\n",
    "for index, row in apple_long_profits.iterrows():\n",
    "    batches = np.floor(cash * port_value) // np.ceil(batch * row[\"Price\"]) # Maximum number of batches of stocks invested in\n",
    "    trade_val = batches * batch * row[\"Price\"] # How much money is put on the line with each trade\n",
    "    if row[\"Low\"] < (1 - stoploss) * row[\"Price\"]:   # Account for the stop-loss\n",
    "        share_profit = np.round((1 - stoploss) * row[\"Price\"], 2)\n",
    "        stop_trig = True\n",
    "    else:\n",
    "        share_profit = row[\"Profit\"]\n",
    "        stop_trig = False\n",
    "    profit = share_profit * batches * batch # Compute profits\n",
    "    # Add a row to the backtest data frame containing the results of the trade\n",
    "    apple_backtest = apple_backtest.append(pd.DataFrame({\n",
    "                \"Start Port. Value\": cash,\n",
    "                \"End Port. Value\": cash + profit,\n",
    "                \"End Date\": row[\"End Date\"],\n",
    "                \"Shares\": batch * batches,\n",
    "                \"Share Price\": row[\"Price\"],\n",
    "                \"Trade Value\": trade_val,\n",
    "                \"Profit per Share\": share_profit,\n",
    "                \"Total Profit\": profit,\n",
    "                \"Stop-Loss Triggered\": stop_trig\n",
    "            }, index = [index]))\n",
    "    cash = max(0, cash + profit)\n",
    " \n",
    "apple_backtest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2108bba6240>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "apple_backtest[\"End Port. Value\"].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ma_crossover_orders(stocks, fast, slow):\n",
    "    \"\"\"\n",
    "    :param stocks: A list of tuples, the first argument in each tuple being a string containing the ticker symbol of each stock (or however you want the stock represented, so long as it's unique), and the second being a pandas DataFrame containing the stocks, with a \"Close\" column and indexing by date (like the data frames returned by the Yahoo! Finance API)\n",
    "    :param fast: Integer for the number of days used in the fast moving average\n",
    "    :param slow: Integer for the number of days used in the slow moving average\n",
    " \n",
    "    :return: pandas DataFrame containing stock orders\n",
    " \n",
    "    This function takes a list of stocks and determines when each stock would be bought or sold depending on a moving average crossover strategy, returning a data frame with information about when the stocks in the portfolio are bought or sold according to the strategy\n",
    "    \"\"\"\n",
    "    fast_str = str(fast) + 'd'\n",
    "    slow_str = str(slow) + 'd'\n",
    "    ma_diff_str = fast_str + '-' + slow_str\n",
    " \n",
    "    trades = pd.DataFrame({\"Price\": [], \"Regime\": [], \"Signal\": []})\n",
    "    for s in stocks:\n",
    "        # Get the moving averages, both fast and slow, along with the difference in the moving averages\n",
    "        s[1][fast_str] = np.round(s[1][\"Close\"].rolling(window = fast, center = False).mean(), 2)\n",
    "        s[1][slow_str] = np.round(s[1][\"Close\"].rolling(window = slow, center = False).mean(), 2)\n",
    "        s[1][ma_diff_str] = s[1][fast_str] - s[1][slow_str]\n",
    " \n",
    "        # np.where() is a vectorized if-else function, where a condition is checked for each component of a vector, and the first argument passed is used when the condition holds, and the other passed if it does not\n",
    "        s[1][\"Regime\"] = np.where(s[1][ma_diff_str] > 0, 1, 0)\n",
    "        # We have 1's for bullish regimes and 0's for everything else. Below I replace bearish regimes's values with -1, and to maintain the rest of the vector, the second argument is apple[\"Regime\"]\n",
    "        s[1][\"Regime\"] = np.where(s[1][ma_diff_str] < 0, -1, s[1][\"Regime\"])\n",
    "        # To ensure that all trades close out, I temporarily change the regime of the last row to 0\n",
    "        regime_orig = s[1].loc[:, \"Regime\"].iloc[-1]\n",
    "        s[1].loc[:, \"Regime\"].iloc[-1] = 0\n",
    "        s[1][\"Signal\"] = np.sign(s[1][\"Regime\"] - s[1][\"Regime\"].shift(1))\n",
    "        # Restore original regime data\n",
    "        s[1].loc[:, \"Regime\"].iloc[-1] = regime_orig\n",
    " \n",
    "        # Get signals\n",
    "        signals = pd.concat([\n",
    "            pd.DataFrame({\"Price\": s[1].loc[s[1][\"Signal\"] == 1, \"Adj. Close\"],\n",
    "                         \"Regime\": s[1].loc[s[1][\"Signal\"] == 1, \"Regime\"],\n",
    "                         \"Signal\": \"Buy\"}),\n",
    "            pd.DataFrame({\"Price\": s[1].loc[s[1][\"Signal\"] == -1, \"Adj. Close\"],\n",
    "                         \"Regime\": s[1].loc[s[1][\"Signal\"] == -1, \"Regime\"],\n",
    "                         \"Signal\": \"Sell\"}),\n",
    "        ])\n",
    "        signals.index = pd.MultiIndex.from_product([signals.index, [s[0]]], names = [\"Date\", \"Symbol\"])\n",
    "        trades = trades.append(signals)\n",
    " \n",
    "    trades.sort_index(inplace = True)\n",
    "    trades.index = pd.MultiIndex.from_tuples(trades.index, names = [\"Date\", \"Symbol\"])\n",
    " \n",
    "    return trades\n",
    " \n",
    " \n",
    "def backtest(signals, cash, port_value = .1, batch = 100):\n",
    "    \"\"\"\n",
    "    :param signals: pandas DataFrame containing buy and sell signals with stock prices and symbols, like that returned by ma_crossover_orders\n",
    "    :param cash: integer for starting cash value\n",
    "    :param port_value: maximum proportion of portfolio to risk on any single trade\n",
    "    :param batch: Trading batch sizes\n",
    " \n",
    "    :return: pandas DataFrame with backtesting results\n",
    " \n",
    "    This function backtests strategies, with the signals generated by the strategies being passed in the signals DataFrame. A fictitious portfolio is simulated and the returns generated by this portfolio are reported.\n",
    "    \"\"\"\n",
    " \n",
    "    SYMBOL = 1 # Constant for which element in index represents symbol\n",
    "    portfolio = dict()    # Will contain how many stocks are in the portfolio for a given symbol\n",
    "    port_prices = dict()  # Tracks old trade prices for determining profits\n",
    "    # Dataframe that will contain backtesting report\n",
    "    results = pd.DataFrame({\"Start Cash\": [],\n",
    "                            \"End Cash\": [],\n",
    "                            \"Portfolio Value\": [],\n",
    "                            \"Type\": [],\n",
    "                            \"Shares\": [],\n",
    "                            \"Share Price\": [],\n",
    "                            \"Trade Value\": [],\n",
    "                            \"Profit per Share\": [],\n",
    "                            \"Total Profit\": []})\n",
    " \n",
    "    for index, row in signals.iterrows():\n",
    "        # These first few lines are done for any trade\n",
    "        shares = portfolio.setdefault(index[SYMBOL], 0)\n",
    "        trade_val = 0\n",
    "        batches = 0\n",
    "        cash_change = row[\"Price\"] * shares   # Shares could potentially be a positive or negative number (cash_change will be added in the end; negative shares indicate a short)\n",
    "        portfolio[index[SYMBOL]] = 0  # For a given symbol, a position is effectively cleared\n",
    " \n",
    "        old_price = port_prices.setdefault(index[SYMBOL], row[\"Price\"])\n",
    "        portfolio_val = 0\n",
    "        for key, val in portfolio.items():\n",
    "            portfolio_val += val * port_prices[key]\n",
    " \n",
    "        if row[\"Signal\"] == \"Buy\" and row[\"Regime\"] == 1:  # Entering a long position\n",
    "            batches = np.floor((portfolio_val + cash) * port_value) // np.ceil(batch * row[\"Price\"]) # Maximum number of batches of stocks invested in\n",
    "            trade_val = batches * batch * row[\"Price\"] # How much money is put on the line with each trade\n",
    "            cash_change -= trade_val  # We are buying shares so cash will go down\n",
    "            portfolio[index[SYMBOL]] = batches * batch  # Recording how many shares are currently invested in the stock\n",
    "            port_prices[index[SYMBOL]] = row[\"Price\"]   # Record price\n",
    "            old_price = row[\"Price\"]\n",
    "        elif row[\"Signal\"] == \"Sell\" and row[\"Regime\"] == -1: # Entering a short\n",
    "            pass\n",
    "            # Do nothing; can we provide a method for shorting the market?\n",
    "        #else:\n",
    "            #raise ValueError(\"I don't know what to do with signal \" + row[\"Signal\"])\n",
    " \n",
    "        pprofit = row[\"Price\"] - old_price   # Compute profit per share; old_price is set in such a way that entering a position results in a profit of zero\n",
    " \n",
    "        # Update report\n",
    "        results = results.append(pd.DataFrame({\n",
    "                \"Start Cash\": cash,\n",
    "                \"End Cash\": cash + cash_change,\n",
    "                \"Portfolio Value\": cash + cash_change + portfolio_val + trade_val,\n",
    "                \"Type\": row[\"Signal\"],\n",
    "                \"Shares\": batch * batches,\n",
    "                \"Share Price\": row[\"Price\"],\n",
    "                \"Trade Value\": abs(cash_change),\n",
    "                \"Profit per Share\": pprofit,\n",
    "                \"Total Profit\": batches * batch * pprofit\n",
    "            }, index = [index]))\n",
    "        cash += cash_change  # Final change to cash balance\n",
    " \n",
    "    results.sort_index(inplace = True)\n",
    "    results.index = pd.MultiIndex.from_tuples(results.index, names = [\"Date\", \"Symbol\"])\n",
    " \n",
    "    return results\n",
    " \n",
    "# Get more stocks\n",
    "# Get more stocks\n",
    "(microsoft, google, facebook, twitter, netflix,\n",
    "amazon, yahoo, ge, qualcomm, ibm, hp) = (quandl.get(\"WIKI/\" + s, start_date=start,\n",
    "                                                                         end_date=end) for s in [\"MSFT\", \"GOOG\", \"FB\", \"TWTR\",\n",
    "                                                                                                 \"NFLX\", \"AMZN\", \"YHOO\", \"GE\",\n",
    "                                                                                                 \"QCOM\", \"IBM\", \"HPQ\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Derek\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:190: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n",
      "C:\\Users\\Derek\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:29: RuntimeWarning: invalid value encountered in sign\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Regime</th>\n",
       "      <th>Signal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th>Symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">2010-03-16</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>28.844953</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMZN</th>\n",
       "      <td>131.790000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GE</th>\n",
       "      <td>14.129260</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HPQ</th>\n",
       "      <td>19.921951</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBM</th>\n",
       "      <td>105.460506</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>23.978839</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NFLX</th>\n",
       "      <td>10.090000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QCOM</th>\n",
       "      <td>32.235226</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YHOO</th>\n",
       "      <td>16.360000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03-17</th>\n",
       "      <th>YHOO</th>\n",
       "      <td>16.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03-24</th>\n",
       "      <th>MSFT</th>\n",
       "      <td>24.207442</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-01</th>\n",
       "      <th>QCOM</th>\n",
       "      <td>34.929069</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-07</th>\n",
       "      <th>QCOM</th>\n",
       "      <td>30.161131</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-10</th>\n",
       "      <th>HPQ</th>\n",
       "      <td>18.684203</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-17</th>\n",
       "      <th>YHOO</th>\n",
       "      <td>16.270000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2010-05-19</th>\n",
       "      <th>AMZN</th>\n",
       "      <td>124.590000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GE</th>\n",
       "      <td>13.495907</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>23.161072</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-20</th>\n",
       "      <th>IBM</th>\n",
       "      <td>102.001194</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-11</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>32.579568</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-18</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>35.222329</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-29</th>\n",
       "      <th>IBM</th>\n",
       "      <td>103.064049</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-30</th>\n",
       "      <th>IBM</th>\n",
       "      <td>101.737540</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-07</th>\n",
       "      <th>IBM</th>\n",
       "      <td>104.637735</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-20</th>\n",
       "      <th>IBM</th>\n",
       "      <td>104.266971</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-22</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>33.288194</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-27</th>\n",
       "      <th>QCOM</th>\n",
       "      <td>32.585294</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-28</th>\n",
       "      <th>IBM</th>\n",
       "      <td>105.815940</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-29</th>\n",
       "      <th>NFLX</th>\n",
       "      <td>14.002857</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-02</th>\n",
       "      <th>HPQ</th>\n",
       "      <td>18.129988</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>166.890000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-06</th>\n",
       "      <th>NFLX</th>\n",
       "      <td>185.300000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-15</th>\n",
       "      <th>HPQ</th>\n",
       "      <td>20.920000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-26</th>\n",
       "      <th>FB</th>\n",
       "      <td>175.990000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <th>FB</th>\n",
       "      <td>184.670000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-09</th>\n",
       "      <th>NFLX</th>\n",
       "      <td>209.310000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-11</th>\n",
       "      <th>HPQ</th>\n",
       "      <td>22.410000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-18</th>\n",
       "      <th>QCOM</th>\n",
       "      <td>68.050000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-19</th>\n",
       "      <th>QCOM</th>\n",
       "      <td>68.040000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-06</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>163.030000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2018-02-21</th>\n",
       "      <th>IBM</th>\n",
       "      <td>153.960000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QCOM</th>\n",
       "      <td>63.400000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-22</th>\n",
       "      <th>HPQ</th>\n",
       "      <td>21.390000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-23</th>\n",
       "      <th>FB</th>\n",
       "      <td>183.290000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-27</th>\n",
       "      <th>GOOG</th>\n",
       "      <td>1118.290000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-08</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>176.940000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-09</th>\n",
       "      <th>HPQ</th>\n",
       "      <td>24.650000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-14</th>\n",
       "      <th>GOOG</th>\n",
       "      <td>1149.490000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-23</th>\n",
       "      <th>GOOG</th>\n",
       "      <td>1021.570000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"11\" valign=\"top\">2018-03-27</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>168.340000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMZN</th>\n",
       "      <td>1497.050000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FB</th>\n",
       "      <td>152.190000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GE</th>\n",
       "      <td>13.440000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>1005.100000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HPQ</th>\n",
       "      <td>21.770000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBM</th>\n",
       "      <td>151.910000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>89.470000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NFLX</th>\n",
       "      <td>300.690000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QCOM</th>\n",
       "      <td>54.840000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWTR</th>\n",
       "      <td>28.070000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Sell</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>509 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Price  Regime Signal\n",
       "Date       Symbol                            \n",
       "2010-03-16 AAPL      28.844953     1.0    Buy\n",
       "           AMZN     131.790000     1.0    Buy\n",
       "           GE        14.129260     1.0    Buy\n",
       "           HPQ       19.921951     1.0    Buy\n",
       "           IBM      105.460506     1.0    Buy\n",
       "           MSFT      23.978839    -1.0   Sell\n",
       "           NFLX      10.090000     1.0    Buy\n",
       "           QCOM      32.235226    -1.0   Sell\n",
       "           YHOO      16.360000    -1.0   Sell\n",
       "2010-03-17 YHOO      16.500000     1.0    Buy\n",
       "2010-03-24 MSFT      24.207442     1.0    Buy\n",
       "2010-04-01 QCOM      34.929069     1.0    Buy\n",
       "2010-05-07 QCOM      30.161131    -1.0   Sell\n",
       "2010-05-10 HPQ       18.684203    -1.0   Sell\n",
       "2010-05-17 YHOO      16.270000    -1.0   Sell\n",
       "2010-05-19 AMZN     124.590000    -1.0   Sell\n",
       "           GE        13.495907    -1.0   Sell\n",
       "           MSFT      23.161072    -1.0   Sell\n",
       "2010-05-20 IBM      102.001194    -1.0   Sell\n",
       "2010-06-11 AAPL      32.579568    -1.0   Sell\n",
       "2010-06-18 AAPL      35.222329     1.0    Buy\n",
       "2010-06-29 IBM      103.064049     1.0    Buy\n",
       "2010-06-30 IBM      101.737540    -1.0   Sell\n",
       "2010-07-07 IBM      104.637735     1.0    Buy\n",
       "2010-07-20 IBM      104.266971    -1.0   Sell\n",
       "2010-07-22 AAPL      33.288194    -1.0   Sell\n",
       "2010-07-27 QCOM      32.585294     1.0    Buy\n",
       "2010-07-28 IBM      105.815940     1.0    Buy\n",
       "2010-07-29 NFLX      14.002857    -1.0   Sell\n",
       "2010-08-02 HPQ       18.129988     1.0    Buy\n",
       "...                        ...     ...    ...\n",
       "2017-11-01 AAPL     166.890000     1.0    Buy\n",
       "2017-12-06 NFLX     185.300000    -1.0   Sell\n",
       "2017-12-15 HPQ       20.920000    -1.0   Sell\n",
       "2017-12-26 FB       175.990000    -1.0   Sell\n",
       "2018-01-03 FB       184.670000     1.0    Buy\n",
       "2018-01-09 NFLX     209.310000     1.0    Buy\n",
       "2018-01-11 HPQ       22.410000     1.0    Buy\n",
       "2018-01-18 QCOM      68.050000    -1.0   Sell\n",
       "2018-01-19 QCOM      68.040000     1.0    Buy\n",
       "2018-02-06 AAPL     163.030000    -1.0   Sell\n",
       "2018-02-21 IBM      153.960000    -1.0   Sell\n",
       "           QCOM      63.400000    -1.0   Sell\n",
       "2018-02-22 HPQ       21.390000    -1.0   Sell\n",
       "2018-02-23 FB       183.290000    -1.0   Sell\n",
       "2018-02-27 GOOG    1118.290000    -1.0   Sell\n",
       "2018-03-08 AAPL     176.940000     1.0    Buy\n",
       "2018-03-09 HPQ       24.650000     1.0    Buy\n",
       "2018-03-14 GOOG    1149.490000     1.0    Buy\n",
       "2018-03-23 GOOG    1021.570000    -1.0   Sell\n",
       "2018-03-27 AAPL     168.340000     1.0   Sell\n",
       "           AMZN    1497.050000     1.0   Sell\n",
       "           FB       152.190000    -1.0    Buy\n",
       "           GE        13.440000    -1.0    Buy\n",
       "           GOOG    1005.100000    -1.0    Buy\n",
       "           HPQ       21.770000     1.0   Sell\n",
       "           IBM      151.910000    -1.0    Buy\n",
       "           MSFT      89.470000     1.0   Sell\n",
       "           NFLX     300.690000     1.0   Sell\n",
       "           QCOM      54.840000    -1.0    Buy\n",
       "           TWTR      28.070000     1.0   Sell\n",
       "\n",
       "[509 rows x 3 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "signals = ma_crossover_orders([(\"AAPL\", apple),\n",
    "                              (\"MSFT\",  microsoft),\n",
    "                              (\"GOOG\",  google),\n",
    "                              (\"FB\",    facebook),\n",
    "                              (\"TWTR\",  twitter),\n",
    "                              (\"NFLX\",  netflix),\n",
    "                              (\"AMZN\",  amazon),\n",
    "                              (\"YHOO\",  yahoo),\n",
    "                              (\"GE\",    ge),\n",
    "                              (\"QCOM\",  qualcomm),\n",
    "                              (\"IBM\",   ibm),\n",
    "                              (\"HPQ\",   hp)],\n",
    "                            fast = 20, slow = 50)\n",
    "signals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Start Cash</th>\n",
       "      <th>End Cash</th>\n",
       "      <th>Portfolio Value</th>\n",
       "      <th>Type</th>\n",
       "      <th>Shares</th>\n",
       "      <th>Share Price</th>\n",
       "      <th>Trade Value</th>\n",
       "      <th>Profit per Share</th>\n",
       "      <th>Total Profit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th>Symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">2010-03-16</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>1.000000e+06</td>\n",
       "      <td>9.019272e+05</td>\n",
       "      <td>1.000000e+06</td>\n",
       "      <td>Buy</td>\n",
       "      <td>3400.0</td>\n",
       "      <td>28.844953</td>\n",
       "      <td>98072.841239</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMZN</th>\n",
       "      <td>9.019272e+05</td>\n",
       "      <td>8.096742e+05</td>\n",
       "      <td>1.000000e+06</td>\n",
       "      <td>Buy</td>\n",
       "      <td>700.0</td>\n",
       "      <td>131.790000</td>\n",
       "      <td>92253.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GE</th>\n",
       "      <td>8.096742e+05</td>\n",
       "      <td>7.107693e+05</td>\n",
       "      <td>1.000000e+06</td>\n",
       "      <td>Buy</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>14.129260</td>\n",
       "      <td>98904.822860</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HPQ</th>\n",
       "      <td>7.107693e+05</td>\n",
       "      <td>6.111596e+05</td>\n",
       "      <td>1.000000e+06</td>\n",
       "      <td>Buy</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>19.921951</td>\n",
       "      <td>99609.756314</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBM</th>\n",
       "      <td>6.111596e+05</td>\n",
       "      <td>5.162451e+05</td>\n",
       "      <td>1.000000e+06</td>\n",
       "      <td>Buy</td>\n",
       "      <td>900.0</td>\n",
       "      <td>105.460506</td>\n",
       "      <td>94914.455453</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>5.162451e+05</td>\n",
       "      <td>5.162451e+05</td>\n",
       "      <td>1.000000e+06</td>\n",
       "      <td>Sell</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.978839</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NFLX</th>\n",
       "      <td>5.162451e+05</td>\n",
       "      <td>4.163541e+05</td>\n",
       "      <td>1.000000e+06</td>\n",
       "      <td>Buy</td>\n",
       "      <td>9900.0</td>\n",
       "      <td>10.090000</td>\n",
       "      <td>99891.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QCOM</th>\n",
       "      <td>4.163541e+05</td>\n",
       "      <td>4.163541e+05</td>\n",
       "      <td>1.000000e+06</td>\n",
       "      <td>Sell</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.235226</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YHOO</th>\n",
       "      <td>4.163541e+05</td>\n",
       "      <td>4.163541e+05</td>\n",
       "      <td>1.000000e+06</td>\n",
       "      <td>Sell</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.360000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03-17</th>\n",
       "      <th>YHOO</th>\n",
       "      <td>4.163541e+05</td>\n",
       "      <td>3.173541e+05</td>\n",
       "      <td>1.000000e+06</td>\n",
       "      <td>Buy</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>16.500000</td>\n",
       "      <td>99000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03-24</th>\n",
       "      <th>MSFT</th>\n",
       "      <td>3.173541e+05</td>\n",
       "      <td>2.181036e+05</td>\n",
       "      <td>1.000000e+06</td>\n",
       "      <td>Buy</td>\n",
       "      <td>4100.0</td>\n",
       "      <td>24.207442</td>\n",
       "      <td>99250.512998</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-01</th>\n",
       "      <th>QCOM</th>\n",
       "      <td>2.181036e+05</td>\n",
       "      <td>1.203022e+05</td>\n",
       "      <td>1.000000e+06</td>\n",
       "      <td>Buy</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>34.929069</td>\n",
       "      <td>97801.393965</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-07</th>\n",
       "      <th>QCOM</th>\n",
       "      <td>1.203022e+05</td>\n",
       "      <td>2.047534e+05</td>\n",
       "      <td>9.866498e+05</td>\n",
       "      <td>Sell</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.161131</td>\n",
       "      <td>84451.168198</td>\n",
       "      <td>-4.767938</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-10</th>\n",
       "      <th>HPQ</th>\n",
       "      <td>2.047534e+05</td>\n",
       "      <td>2.981744e+05</td>\n",
       "      <td>9.804610e+05</td>\n",
       "      <td>Sell</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.684203</td>\n",
       "      <td>93421.012620</td>\n",
       "      <td>-1.237749</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-17</th>\n",
       "      <th>YHOO</th>\n",
       "      <td>2.981744e+05</td>\n",
       "      <td>3.957944e+05</td>\n",
       "      <td>9.790810e+05</td>\n",
       "      <td>Sell</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.270000</td>\n",
       "      <td>97620.000000</td>\n",
       "      <td>-0.230000</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2010-05-19</th>\n",
       "      <th>AMZN</th>\n",
       "      <td>3.957944e+05</td>\n",
       "      <td>4.830074e+05</td>\n",
       "      <td>9.740410e+05</td>\n",
       "      <td>Sell</td>\n",
       "      <td>0.0</td>\n",
       "      <td>124.590000</td>\n",
       "      <td>87213.000000</td>\n",
       "      <td>-7.200000</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GE</th>\n",
       "      <td>4.830074e+05</td>\n",
       "      <td>5.774787e+05</td>\n",
       "      <td>9.696076e+05</td>\n",
       "      <td>Sell</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.495907</td>\n",
       "      <td>94471.347126</td>\n",
       "      <td>-0.633354</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>5.774787e+05</td>\n",
       "      <td>6.724391e+05</td>\n",
       "      <td>9.653174e+05</td>\n",
       "      <td>Sell</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.161072</td>\n",
       "      <td>94960.396545</td>\n",
       "      <td>-1.046370</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-20</th>\n",
       "      <th>IBM</th>\n",
       "      <td>6.724391e+05</td>\n",
       "      <td>7.642402e+05</td>\n",
       "      <td>9.622041e+05</td>\n",
       "      <td>Sell</td>\n",
       "      <td>0.0</td>\n",
       "      <td>102.001194</td>\n",
       "      <td>91801.074363</td>\n",
       "      <td>-3.459312</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-11</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>7.642402e+05</td>\n",
       "      <td>8.750107e+05</td>\n",
       "      <td>9.749017e+05</td>\n",
       "      <td>Sell</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.579568</td>\n",
       "      <td>110770.532335</td>\n",
       "      <td>3.734615</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-18</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>8.750107e+05</td>\n",
       "      <td>7.799105e+05</td>\n",
       "      <td>9.749017e+05</td>\n",
       "      <td>Buy</td>\n",
       "      <td>2700.0</td>\n",
       "      <td>35.222329</td>\n",
       "      <td>95100.288159</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-29</th>\n",
       "      <th>IBM</th>\n",
       "      <td>7.799105e+05</td>\n",
       "      <td>6.871528e+05</td>\n",
       "      <td>9.749017e+05</td>\n",
       "      <td>Buy</td>\n",
       "      <td>900.0</td>\n",
       "      <td>103.064049</td>\n",
       "      <td>92757.644524</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-30</th>\n",
       "      <th>IBM</th>\n",
       "      <td>6.871528e+05</td>\n",
       "      <td>7.787166e+05</td>\n",
       "      <td>9.737079e+05</td>\n",
       "      <td>Sell</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101.737540</td>\n",
       "      <td>91563.785641</td>\n",
       "      <td>-1.326510</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-07</th>\n",
       "      <th>IBM</th>\n",
       "      <td>7.787166e+05</td>\n",
       "      <td>6.845426e+05</td>\n",
       "      <td>9.737079e+05</td>\n",
       "      <td>Buy</td>\n",
       "      <td>900.0</td>\n",
       "      <td>104.637735</td>\n",
       "      <td>94173.961584</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-20</th>\n",
       "      <th>IBM</th>\n",
       "      <td>6.845426e+05</td>\n",
       "      <td>7.783829e+05</td>\n",
       "      <td>9.733742e+05</td>\n",
       "      <td>Sell</td>\n",
       "      <td>0.0</td>\n",
       "      <td>104.266971</td>\n",
       "      <td>93840.274319</td>\n",
       "      <td>-0.370764</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-22</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>7.783829e+05</td>\n",
       "      <td>8.682610e+05</td>\n",
       "      <td>9.681520e+05</td>\n",
       "      <td>Sell</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.288194</td>\n",
       "      <td>89878.124302</td>\n",
       "      <td>-1.934135</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-27</th>\n",
       "      <th>QCOM</th>\n",
       "      <td>8.682610e+05</td>\n",
       "      <td>7.737637e+05</td>\n",
       "      <td>9.681520e+05</td>\n",
       "      <td>Buy</td>\n",
       "      <td>2900.0</td>\n",
       "      <td>32.585294</td>\n",
       "      <td>94497.352787</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-28</th>\n",
       "      <th>IBM</th>\n",
       "      <td>7.737637e+05</td>\n",
       "      <td>6.785293e+05</td>\n",
       "      <td>9.681520e+05</td>\n",
       "      <td>Buy</td>\n",
       "      <td>900.0</td>\n",
       "      <td>105.815940</td>\n",
       "      <td>95234.345561</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-29</th>\n",
       "      <th>NFLX</th>\n",
       "      <td>6.785293e+05</td>\n",
       "      <td>8.171576e+05</td>\n",
       "      <td>1.006889e+06</td>\n",
       "      <td>Sell</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.002857</td>\n",
       "      <td>138628.285714</td>\n",
       "      <td>3.912857</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-02</th>\n",
       "      <th>HPQ</th>\n",
       "      <td>8.171576e+05</td>\n",
       "      <td>7.174427e+05</td>\n",
       "      <td>1.006889e+06</td>\n",
       "      <td>Buy</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>18.129988</td>\n",
       "      <td>99714.934419</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>3.300472e+05</td>\n",
       "      <td>1.297792e+05</td>\n",
       "      <td>2.153164e+06</td>\n",
       "      <td>Buy</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>166.890000</td>\n",
       "      <td>200268.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-06</th>\n",
       "      <th>NFLX</th>\n",
       "      <td>1.297792e+05</td>\n",
       "      <td>3.336092e+05</td>\n",
       "      <td>2.149600e+06</td>\n",
       "      <td>Sell</td>\n",
       "      <td>0.0</td>\n",
       "      <td>185.300000</td>\n",
       "      <td>203830.000000</td>\n",
       "      <td>-3.240000</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-15</th>\n",
       "      <th>HPQ</th>\n",
       "      <td>3.336092e+05</td>\n",
       "      <td>5.700052e+05</td>\n",
       "      <td>2.170395e+06</td>\n",
       "      <td>Sell</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.920000</td>\n",
       "      <td>236396.000000</td>\n",
       "      <td>1.840267</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-26</th>\n",
       "      <th>FB</th>\n",
       "      <td>5.700052e+05</td>\n",
       "      <td>8.339902e+05</td>\n",
       "      <td>2.244450e+06</td>\n",
       "      <td>Sell</td>\n",
       "      <td>0.0</td>\n",
       "      <td>175.990000</td>\n",
       "      <td>263985.000000</td>\n",
       "      <td>49.370000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <th>FB</th>\n",
       "      <td>8.339902e+05</td>\n",
       "      <td>6.123862e+05</td>\n",
       "      <td>2.244450e+06</td>\n",
       "      <td>Buy</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>184.670000</td>\n",
       "      <td>221604.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-09</th>\n",
       "      <th>NFLX</th>\n",
       "      <td>6.123862e+05</td>\n",
       "      <td>4.030762e+05</td>\n",
       "      <td>2.244450e+06</td>\n",
       "      <td>Buy</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>209.310000</td>\n",
       "      <td>209310.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-11</th>\n",
       "      <th>HPQ</th>\n",
       "      <td>4.030762e+05</td>\n",
       "      <td>1.789762e+05</td>\n",
       "      <td>2.244450e+06</td>\n",
       "      <td>Buy</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>22.410000</td>\n",
       "      <td>224100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-18</th>\n",
       "      <th>QCOM</th>\n",
       "      <td>1.789762e+05</td>\n",
       "      <td>4.511762e+05</td>\n",
       "      <td>2.301960e+06</td>\n",
       "      <td>Sell</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.050000</td>\n",
       "      <td>272200.000000</td>\n",
       "      <td>14.377402</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-19</th>\n",
       "      <th>QCOM</th>\n",
       "      <td>4.511762e+05</td>\n",
       "      <td>2.266442e+05</td>\n",
       "      <td>2.301960e+06</td>\n",
       "      <td>Buy</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>68.040000</td>\n",
       "      <td>224532.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-06</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>2.266442e+05</td>\n",
       "      <td>4.222802e+05</td>\n",
       "      <td>2.297328e+06</td>\n",
       "      <td>Sell</td>\n",
       "      <td>0.0</td>\n",
       "      <td>163.030000</td>\n",
       "      <td>195636.000000</td>\n",
       "      <td>-3.860000</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2018-02-21</th>\n",
       "      <th>IBM</th>\n",
       "      <td>4.222802e+05</td>\n",
       "      <td>6.378242e+05</td>\n",
       "      <td>2.309716e+06</td>\n",
       "      <td>Sell</td>\n",
       "      <td>0.0</td>\n",
       "      <td>153.960000</td>\n",
       "      <td>215544.000000</td>\n",
       "      <td>8.848221</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QCOM</th>\n",
       "      <td>6.378242e+05</td>\n",
       "      <td>8.470442e+05</td>\n",
       "      <td>2.294404e+06</td>\n",
       "      <td>Sell</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63.400000</td>\n",
       "      <td>209220.000000</td>\n",
       "      <td>-4.640000</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-22</th>\n",
       "      <th>HPQ</th>\n",
       "      <td>8.470442e+05</td>\n",
       "      <td>1.060944e+06</td>\n",
       "      <td>2.284204e+06</td>\n",
       "      <td>Sell</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.390000</td>\n",
       "      <td>213900.000000</td>\n",
       "      <td>-1.020000</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-23</th>\n",
       "      <th>FB</th>\n",
       "      <td>1.060944e+06</td>\n",
       "      <td>1.280892e+06</td>\n",
       "      <td>2.282548e+06</td>\n",
       "      <td>Sell</td>\n",
       "      <td>0.0</td>\n",
       "      <td>183.290000</td>\n",
       "      <td>219948.000000</td>\n",
       "      <td>-1.380000</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-27</th>\n",
       "      <th>GOOG</th>\n",
       "      <td>1.280892e+06</td>\n",
       "      <td>1.504550e+06</td>\n",
       "      <td>2.316306e+06</td>\n",
       "      <td>Sell</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1118.290000</td>\n",
       "      <td>223658.000000</td>\n",
       "      <td>168.790000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-08</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>1.504550e+06</td>\n",
       "      <td>1.274528e+06</td>\n",
       "      <td>2.316306e+06</td>\n",
       "      <td>Buy</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>176.940000</td>\n",
       "      <td>230022.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-09</th>\n",
       "      <th>HPQ</th>\n",
       "      <td>1.274528e+06</td>\n",
       "      <td>1.045283e+06</td>\n",
       "      <td>2.316306e+06</td>\n",
       "      <td>Buy</td>\n",
       "      <td>9300.0</td>\n",
       "      <td>24.650000</td>\n",
       "      <td>229245.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-14</th>\n",
       "      <th>GOOG</th>\n",
       "      <td>1.045283e+06</td>\n",
       "      <td>8.153852e+05</td>\n",
       "      <td>2.316306e+06</td>\n",
       "      <td>Buy</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1149.490000</td>\n",
       "      <td>229898.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-23</th>\n",
       "      <th>GOOG</th>\n",
       "      <td>8.153852e+05</td>\n",
       "      <td>1.019699e+06</td>\n",
       "      <td>2.290722e+06</td>\n",
       "      <td>Sell</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1021.570000</td>\n",
       "      <td>204314.000000</td>\n",
       "      <td>-127.920000</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"11\" valign=\"top\">2018-03-27</th>\n",
       "      <th>AAPL</th>\n",
       "      <td>1.019699e+06</td>\n",
       "      <td>1.238541e+06</td>\n",
       "      <td>2.279542e+06</td>\n",
       "      <td>Sell</td>\n",
       "      <td>0.0</td>\n",
       "      <td>168.340000</td>\n",
       "      <td>218842.000000</td>\n",
       "      <td>-8.600000</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMZN</th>\n",
       "      <td>1.238541e+06</td>\n",
       "      <td>1.537951e+06</td>\n",
       "      <td>2.377684e+06</td>\n",
       "      <td>Sell</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1497.050000</td>\n",
       "      <td>299410.000000</td>\n",
       "      <td>490.710000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FB</th>\n",
       "      <td>1.537951e+06</td>\n",
       "      <td>1.537951e+06</td>\n",
       "      <td>2.377684e+06</td>\n",
       "      <td>Buy</td>\n",
       "      <td>0.0</td>\n",
       "      <td>152.190000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-32.480000</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GE</th>\n",
       "      <td>1.537951e+06</td>\n",
       "      <td>1.537951e+06</td>\n",
       "      <td>2.377684e+06</td>\n",
       "      <td>Buy</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.440000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-16.213194</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>1.537951e+06</td>\n",
       "      <td>1.537951e+06</td>\n",
       "      <td>2.377684e+06</td>\n",
       "      <td>Buy</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1005.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-144.390000</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HPQ</th>\n",
       "      <td>1.537951e+06</td>\n",
       "      <td>1.740412e+06</td>\n",
       "      <td>2.350900e+06</td>\n",
       "      <td>Sell</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.770000</td>\n",
       "      <td>202461.000000</td>\n",
       "      <td>-2.880000</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBM</th>\n",
       "      <td>1.740412e+06</td>\n",
       "      <td>1.740412e+06</td>\n",
       "      <td>2.350900e+06</td>\n",
       "      <td>Buy</td>\n",
       "      <td>0.0</td>\n",
       "      <td>151.910000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.798221</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>1.740412e+06</td>\n",
       "      <td>2.026716e+06</td>\n",
       "      <td>2.451672e+06</td>\n",
       "      <td>Sell</td>\n",
       "      <td>0.0</td>\n",
       "      <td>89.470000</td>\n",
       "      <td>286304.000000</td>\n",
       "      <td>31.491454</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NFLX</th>\n",
       "      <td>2.026716e+06</td>\n",
       "      <td>2.327406e+06</td>\n",
       "      <td>2.543052e+06</td>\n",
       "      <td>Sell</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.690000</td>\n",
       "      <td>300690.000000</td>\n",
       "      <td>91.380000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QCOM</th>\n",
       "      <td>2.327406e+06</td>\n",
       "      <td>2.327406e+06</td>\n",
       "      <td>2.543052e+06</td>\n",
       "      <td>Buy</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54.840000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-13.200000</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWTR</th>\n",
       "      <td>2.327406e+06</td>\n",
       "      <td>2.683895e+06</td>\n",
       "      <td>2.683895e+06</td>\n",
       "      <td>Sell</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.070000</td>\n",
       "      <td>356489.000000</td>\n",
       "      <td>11.090000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>509 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Start Cash      End Cash  Portfolio Value  Type   Shares  \\\n",
       "Date       Symbol                                                               \n",
       "2010-03-16 AAPL    1.000000e+06  9.019272e+05     1.000000e+06   Buy   3400.0   \n",
       "           AMZN    9.019272e+05  8.096742e+05     1.000000e+06   Buy    700.0   \n",
       "           GE      8.096742e+05  7.107693e+05     1.000000e+06   Buy   7000.0   \n",
       "           HPQ     7.107693e+05  6.111596e+05     1.000000e+06   Buy   5000.0   \n",
       "           IBM     6.111596e+05  5.162451e+05     1.000000e+06   Buy    900.0   \n",
       "           MSFT    5.162451e+05  5.162451e+05     1.000000e+06  Sell      0.0   \n",
       "           NFLX    5.162451e+05  4.163541e+05     1.000000e+06   Buy   9900.0   \n",
       "           QCOM    4.163541e+05  4.163541e+05     1.000000e+06  Sell      0.0   \n",
       "           YHOO    4.163541e+05  4.163541e+05     1.000000e+06  Sell      0.0   \n",
       "2010-03-17 YHOO    4.163541e+05  3.173541e+05     1.000000e+06   Buy   6000.0   \n",
       "2010-03-24 MSFT    3.173541e+05  2.181036e+05     1.000000e+06   Buy   4100.0   \n",
       "2010-04-01 QCOM    2.181036e+05  1.203022e+05     1.000000e+06   Buy   2800.0   \n",
       "2010-05-07 QCOM    1.203022e+05  2.047534e+05     9.866498e+05  Sell      0.0   \n",
       "2010-05-10 HPQ     2.047534e+05  2.981744e+05     9.804610e+05  Sell      0.0   \n",
       "2010-05-17 YHOO    2.981744e+05  3.957944e+05     9.790810e+05  Sell      0.0   \n",
       "2010-05-19 AMZN    3.957944e+05  4.830074e+05     9.740410e+05  Sell      0.0   \n",
       "           GE      4.830074e+05  5.774787e+05     9.696076e+05  Sell      0.0   \n",
       "           MSFT    5.774787e+05  6.724391e+05     9.653174e+05  Sell      0.0   \n",
       "2010-05-20 IBM     6.724391e+05  7.642402e+05     9.622041e+05  Sell      0.0   \n",
       "2010-06-11 AAPL    7.642402e+05  8.750107e+05     9.749017e+05  Sell      0.0   \n",
       "2010-06-18 AAPL    8.750107e+05  7.799105e+05     9.749017e+05   Buy   2700.0   \n",
       "2010-06-29 IBM     7.799105e+05  6.871528e+05     9.749017e+05   Buy    900.0   \n",
       "2010-06-30 IBM     6.871528e+05  7.787166e+05     9.737079e+05  Sell      0.0   \n",
       "2010-07-07 IBM     7.787166e+05  6.845426e+05     9.737079e+05   Buy    900.0   \n",
       "2010-07-20 IBM     6.845426e+05  7.783829e+05     9.733742e+05  Sell      0.0   \n",
       "2010-07-22 AAPL    7.783829e+05  8.682610e+05     9.681520e+05  Sell      0.0   \n",
       "2010-07-27 QCOM    8.682610e+05  7.737637e+05     9.681520e+05   Buy   2900.0   \n",
       "2010-07-28 IBM     7.737637e+05  6.785293e+05     9.681520e+05   Buy    900.0   \n",
       "2010-07-29 NFLX    6.785293e+05  8.171576e+05     1.006889e+06  Sell      0.0   \n",
       "2010-08-02 HPQ     8.171576e+05  7.174427e+05     1.006889e+06   Buy   5500.0   \n",
       "...                         ...           ...              ...   ...      ...   \n",
       "2017-11-01 AAPL    3.300472e+05  1.297792e+05     2.153164e+06   Buy   1200.0   \n",
       "2017-12-06 NFLX    1.297792e+05  3.336092e+05     2.149600e+06  Sell      0.0   \n",
       "2017-12-15 HPQ     3.336092e+05  5.700052e+05     2.170395e+06  Sell      0.0   \n",
       "2017-12-26 FB      5.700052e+05  8.339902e+05     2.244450e+06  Sell      0.0   \n",
       "2018-01-03 FB      8.339902e+05  6.123862e+05     2.244450e+06   Buy   1200.0   \n",
       "2018-01-09 NFLX    6.123862e+05  4.030762e+05     2.244450e+06   Buy   1000.0   \n",
       "2018-01-11 HPQ     4.030762e+05  1.789762e+05     2.244450e+06   Buy  10000.0   \n",
       "2018-01-18 QCOM    1.789762e+05  4.511762e+05     2.301960e+06  Sell      0.0   \n",
       "2018-01-19 QCOM    4.511762e+05  2.266442e+05     2.301960e+06   Buy   3300.0   \n",
       "2018-02-06 AAPL    2.266442e+05  4.222802e+05     2.297328e+06  Sell      0.0   \n",
       "2018-02-21 IBM     4.222802e+05  6.378242e+05     2.309716e+06  Sell      0.0   \n",
       "           QCOM    6.378242e+05  8.470442e+05     2.294404e+06  Sell      0.0   \n",
       "2018-02-22 HPQ     8.470442e+05  1.060944e+06     2.284204e+06  Sell      0.0   \n",
       "2018-02-23 FB      1.060944e+06  1.280892e+06     2.282548e+06  Sell      0.0   \n",
       "2018-02-27 GOOG    1.280892e+06  1.504550e+06     2.316306e+06  Sell      0.0   \n",
       "2018-03-08 AAPL    1.504550e+06  1.274528e+06     2.316306e+06   Buy   1300.0   \n",
       "2018-03-09 HPQ     1.274528e+06  1.045283e+06     2.316306e+06   Buy   9300.0   \n",
       "2018-03-14 GOOG    1.045283e+06  8.153852e+05     2.316306e+06   Buy    200.0   \n",
       "2018-03-23 GOOG    8.153852e+05  1.019699e+06     2.290722e+06  Sell      0.0   \n",
       "2018-03-27 AAPL    1.019699e+06  1.238541e+06     2.279542e+06  Sell      0.0   \n",
       "           AMZN    1.238541e+06  1.537951e+06     2.377684e+06  Sell      0.0   \n",
       "           FB      1.537951e+06  1.537951e+06     2.377684e+06   Buy      0.0   \n",
       "           GE      1.537951e+06  1.537951e+06     2.377684e+06   Buy      0.0   \n",
       "           GOOG    1.537951e+06  1.537951e+06     2.377684e+06   Buy      0.0   \n",
       "           HPQ     1.537951e+06  1.740412e+06     2.350900e+06  Sell      0.0   \n",
       "           IBM     1.740412e+06  1.740412e+06     2.350900e+06   Buy      0.0   \n",
       "           MSFT    1.740412e+06  2.026716e+06     2.451672e+06  Sell      0.0   \n",
       "           NFLX    2.026716e+06  2.327406e+06     2.543052e+06  Sell      0.0   \n",
       "           QCOM    2.327406e+06  2.327406e+06     2.543052e+06   Buy      0.0   \n",
       "           TWTR    2.327406e+06  2.683895e+06     2.683895e+06  Sell      0.0   \n",
       "\n",
       "                   Share Price    Trade Value  Profit per Share  Total Profit  \n",
       "Date       Symbol                                                              \n",
       "2010-03-16 AAPL      28.844953   98072.841239          0.000000           0.0  \n",
       "           AMZN     131.790000   92253.000000          0.000000           0.0  \n",
       "           GE        14.129260   98904.822860          0.000000           0.0  \n",
       "           HPQ       19.921951   99609.756314          0.000000           0.0  \n",
       "           IBM      105.460506   94914.455453          0.000000           0.0  \n",
       "           MSFT      23.978839       0.000000          0.000000           0.0  \n",
       "           NFLX      10.090000   99891.000000          0.000000           0.0  \n",
       "           QCOM      32.235226       0.000000          0.000000           0.0  \n",
       "           YHOO      16.360000       0.000000          0.000000           0.0  \n",
       "2010-03-17 YHOO      16.500000   99000.000000          0.000000           0.0  \n",
       "2010-03-24 MSFT      24.207442   99250.512998          0.000000           0.0  \n",
       "2010-04-01 QCOM      34.929069   97801.393965          0.000000           0.0  \n",
       "2010-05-07 QCOM      30.161131   84451.168198         -4.767938          -0.0  \n",
       "2010-05-10 HPQ       18.684203   93421.012620         -1.237749          -0.0  \n",
       "2010-05-17 YHOO      16.270000   97620.000000         -0.230000          -0.0  \n",
       "2010-05-19 AMZN     124.590000   87213.000000         -7.200000          -0.0  \n",
       "           GE        13.495907   94471.347126         -0.633354          -0.0  \n",
       "           MSFT      23.161072   94960.396545         -1.046370          -0.0  \n",
       "2010-05-20 IBM      102.001194   91801.074363         -3.459312          -0.0  \n",
       "2010-06-11 AAPL      32.579568  110770.532335          3.734615           0.0  \n",
       "2010-06-18 AAPL      35.222329   95100.288159          0.000000           0.0  \n",
       "2010-06-29 IBM      103.064049   92757.644524          0.000000           0.0  \n",
       "2010-06-30 IBM      101.737540   91563.785641         -1.326510          -0.0  \n",
       "2010-07-07 IBM      104.637735   94173.961584          0.000000           0.0  \n",
       "2010-07-20 IBM      104.266971   93840.274319         -0.370764          -0.0  \n",
       "2010-07-22 AAPL      33.288194   89878.124302         -1.934135          -0.0  \n",
       "2010-07-27 QCOM      32.585294   94497.352787          0.000000           0.0  \n",
       "2010-07-28 IBM      105.815940   95234.345561          0.000000           0.0  \n",
       "2010-07-29 NFLX      14.002857  138628.285714          3.912857           0.0  \n",
       "2010-08-02 HPQ       18.129988   99714.934419          0.000000           0.0  \n",
       "...                        ...            ...               ...           ...  \n",
       "2017-11-01 AAPL     166.890000  200268.000000          0.000000           0.0  \n",
       "2017-12-06 NFLX     185.300000  203830.000000         -3.240000          -0.0  \n",
       "2017-12-15 HPQ       20.920000  236396.000000          1.840267           0.0  \n",
       "2017-12-26 FB       175.990000  263985.000000         49.370000           0.0  \n",
       "2018-01-03 FB       184.670000  221604.000000          0.000000           0.0  \n",
       "2018-01-09 NFLX     209.310000  209310.000000          0.000000           0.0  \n",
       "2018-01-11 HPQ       22.410000  224100.000000          0.000000           0.0  \n",
       "2018-01-18 QCOM      68.050000  272200.000000         14.377402           0.0  \n",
       "2018-01-19 QCOM      68.040000  224532.000000          0.000000           0.0  \n",
       "2018-02-06 AAPL     163.030000  195636.000000         -3.860000          -0.0  \n",
       "2018-02-21 IBM      153.960000  215544.000000          8.848221           0.0  \n",
       "           QCOM      63.400000  209220.000000         -4.640000          -0.0  \n",
       "2018-02-22 HPQ       21.390000  213900.000000         -1.020000          -0.0  \n",
       "2018-02-23 FB       183.290000  219948.000000         -1.380000          -0.0  \n",
       "2018-02-27 GOOG    1118.290000  223658.000000        168.790000           0.0  \n",
       "2018-03-08 AAPL     176.940000  230022.000000          0.000000           0.0  \n",
       "2018-03-09 HPQ       24.650000  229245.000000          0.000000           0.0  \n",
       "2018-03-14 GOOG    1149.490000  229898.000000          0.000000           0.0  \n",
       "2018-03-23 GOOG    1021.570000  204314.000000       -127.920000          -0.0  \n",
       "2018-03-27 AAPL     168.340000  218842.000000         -8.600000          -0.0  \n",
       "           AMZN    1497.050000  299410.000000        490.710000           0.0  \n",
       "           FB       152.190000       0.000000        -32.480000          -0.0  \n",
       "           GE        13.440000       0.000000        -16.213194          -0.0  \n",
       "           GOOG    1005.100000       0.000000       -144.390000          -0.0  \n",
       "           HPQ       21.770000  202461.000000         -2.880000          -0.0  \n",
       "           IBM      151.910000       0.000000          6.798221           0.0  \n",
       "           MSFT      89.470000  286304.000000         31.491454           0.0  \n",
       "           NFLX     300.690000  300690.000000         91.380000           0.0  \n",
       "           QCOM      54.840000       0.000000        -13.200000          -0.0  \n",
       "           TWTR      28.070000  356489.000000         11.090000           0.0  \n",
       "\n",
       "[509 rows x 9 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bk = backtest(signals, 1000000)\n",
    "bk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2108bbcc390>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bk[\"Portfolio Value\"].groupby(level = 0).apply(lambda x: x[-1]).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-06-01</th>\n",
       "      <td>211.94</td>\n",
       "      <td>212.34</td>\n",
       "      <td>210.620</td>\n",
       "      <td>211.57</td>\n",
       "      <td>211.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30</th>\n",
       "      <td>279.11</td>\n",
       "      <td>280.04</td>\n",
       "      <td>277.805</td>\n",
       "      <td>279.03</td>\n",
       "      <td>279.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High      Low   Close  Adj Close\n",
       "date                                                  \n",
       "2015-06-01  211.94  212.34  210.620  211.57     211.57\n",
       "2019-05-30  279.11  280.04  277.805  279.03     279.03"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#spyder = web.DataReader(\"SPY\", \"yahoo\", start, end)\n",
    "spyder = spyderdat.loc[start:end]\n",
    "spyder.iloc[[0,-1],:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1317061.9999999998"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batches = 1000000 // np.ceil(100 * spyder.loc[:,\"Adj Close\"].iloc[0]) # Maximum number of batches of stocks invested in\n",
    "trade_val = batches * batch * spyder.loc[:,\"Adj Close\"].iloc[0] # How much money is used to buy SPY\n",
    "final_val = batches * batch * spyder.loc[:,\"Adj Close\"].iloc[-1] + (1000000 - trade_val) # Final value of the portfolio\n",
    "final_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2108c7ff710>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We see that the buy-and-hold strategy beats the strategy we developed earlier. I would also like to see a plot.\n",
    "ax_bench = (spyder[\"Adj Close\"] / spyder.loc[:, \"Adj Close\"].iloc[0]).plot(label = \"SPY\")\n",
    "ax_bench = (bk[\"Portfolio Value\"].groupby(level = 0).apply(lambda x: x[-1]) / 1000000).plot(ax = ax_bench, label = \"Portfolio\")\n",
    "ax_bench.legend(ax_bench.get_lines(), [l.get_label() for l in ax_bench.get_lines()], loc = 'best')\n",
    "ax_bench"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
